Review Reports - Artificial Intelligence Readiness in Nursing: A Scoping Review of Knowledge, Attitudes, Practice, and Emerging Evidence on Patient Safety Culture

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for allowig me to review this wonderful and timely sensitive article. My comments are below:

I would recommend making the title more catchy to increase visibility and be able to dessiminate your findings
I would move the number of the articles from methodology in the abstract to results
I would recommend toning down the conculsion about patient safety as it is mentioned only in one study and align your conclusion with your findings!
Do you need subheadings for the introduction? It is well organized but made the study seems more of a narrative review.
The more I read your methodology, the more I am convinced this is a systematic review. Please reconsider your methodogy.
Can you justify including a systematic review in your review, please?
Did any of the study addressed concerns about digital divide and equitable access to new technology as a barrier of AI adoption?
I know age was an enabling factor, how about gender, LMIC vs high income countries and spoken language? Were they addressed in the included studies?
Did any study voiced concerns about AI hallucinations and accuracy?
Did any study address medicolegal aspects and liability?
Do you need to write discussion with multiple subheadings? I encourage you to reconsider making it one section with multiple paragraphs
Good job on the limitation section, well written
The conclusion stated the review covers all healthworkers, that's not true. It only focuses on nurses. Please be specific
Make sure you address the findings in your conclusion and write maybe one or two sentences about lack of safety culture rather than a whole paragraph.

I think you have done the work and this paper should be written as a systematic review to make the evidence stronger. Keep up the good work.

Author Response

Response to Reviewer #1 Comments

We sincerely thank Reviewer #1 for the thoughtful and constructive feedback. We have carefully revised the manuscript in line with the recommendations. Below is our detailed, point‑by‑point response.

Comment 1: I would recommend making the title more catchy to increase visibility and be able to dessiminate your findings

Response 1: We revised the title to make it more concise, engaging, and reflective of the study’s contribution. The updated title emphasizes both AI readiness and patient safety culture, improving clarity and visibility. Revised title: “Artificial Intelligence Readiness in Nursing: A Scoping Review of Knowledge, Attitudes, Practice, and Emerging Evidence on Patient Safety Culture.” This was further revised to avoid overstating the strength of evidence on patient safety culture as also suggested in Major Comment 1 of Reviewer #2. Please refer to Page 1, Lines 1-4.

Comment 2: I would move the number of the articles from methodology in the abstract to results

Response 2: We updated the abstract so that the number of included studies (n = 10) now appears in the Results section rather than the Methods, aligning with standard reporting conventions for scoping reviews. Please refer to Page 1, Line/s 28.

Comment 3: I would recommend toning down the conculsion about patient safety as it is mentioned only in one study and align your conclusion with your findings!

Response 3: We revised the conclusion to avoid overstating the evidence. The updated text now clearly states that only one included study examined patient safety culture, and therefore conclusions regarding safety culture are limited. We added a brief, balanced statement acknowledging the gap in evidence. Please refer to Page 1, Abstract in Lines 36-39, and Page 19 in the Conclusions section, last paragraph.

Comment 4: Do you need subheadings for the introduction? It is well organized but made the study seems more of a narrative review.

Response 4: We removed the subheadings in the Introduction and integrated the content into a cohesive narrative. This improves flow and aligns the structure with typical scoping review introductions. Please refer to Pages 2-3, Lines 43-118.

Comment 5: The more I read your methodology, the more I am convinced this is a systematic review. Please reconsider your methodogy.

Response 5: We clarified throughout the Methods section that the study follows Arksey & O’Malley’s scoping review framework, enhanced by Levac et al., and adheres to PRISMA‑ScR. We emphasized that the purpose is mapping evidence, not evaluating or synthesizing effectiveness, which distinguishes it from a systematic review. This clarification preserves methodological accuracy while addressing the reviewer’s concern. Hence, we maintained the methodology as scoping review. Please refer to Pages 3-4 for more justified information, specifically in Lines 128-141 and 187-191.

Comment 6: Can you justify including a systematic review in your review, please?

Response 6: We added justification explaining that scoping reviews may include systematic reviews when they provide relevant evidence on the topic. The included systematic review contributed important data on nurses’ AI knowledge and attitudes across multiple countries, enriching the mapping of global evidence. Please refer to Page 4 in Lines 187-191.

Comment 7: Did any of the study addressed concerns about digital divide and equitable access to new technology as a barrier of AI adoption?

Response 7: We did not add a statement in the Results and Discussion noting that none of the included studies explicitly examined digital divide, infrastructure inequities, or equitable access to AI technologies. This comment did not result in any revisions.

Comment 8: I know age was an enabling factor, how about gender, LMIC vs high income countries and spoken language? Were they addressed in the included studies?

Response 8: We updated the Results to clarify:

Gender: Gender was significantly associated with knowledge in at least one included study in the systematic review of Sandanasamy et al., hence there is lack of evidence. This was not added in the synthesis of the results.
Country income level: This was not addressed in the included studies; hence no revision was required.
Language: No study examined spoken language as a factor influencing AI perceptions. No revision was required.

Comment 9: Did any study voiced concerns about AI hallucinations and accuracy?

Response 9: We did not add a statement indicating that none of the included studies explicitly discussed AI hallucinations, a concept more commonly associated with large language models. No revision was required.

Comment 10: Did any study address medicolegal aspects and liability?

Response 10: We sincerely clarify that no included study directly addressed medicolegal liability related to AI use. Ethical concerns such as privacy and job displacement were common, but legal accountability was not explored. No revision was required.

Comment 11: Do you need to write discussion with multiple subheadings? I encourage you to reconsider making it one section with multiple paragraphs

Response 11: We revised the Discussion by removing multiple subheadings and restructuring it into a single, cohesive narrative with clear thematic flow. This improves readability and aligns with both reviewers’ recommendations. Please refer to Pages 15-17.

Comment 12: Good job on the limitation section, well written

Response 12: We appreciate the positive feedback and have retained the section as written with minor revision based on the comment of Reviewer #2.

Comment 13: The conclusion stated the review covers all healthworkers, that's not true. It only focuses on nurses. Please be specific

Response 13: We corrected the conclusion to explicitly state that the review focuses on registered nurses, with occasional inclusion of nursing students and mixed healthcare worker samples only when nurses’ data were extractable.

Comment 14: Make sure you address the findings in your conclusion and write maybe one or two sentences about lack of safety culture rather than a whole paragraph.

Response 14: We revised the conclusion (Please refer to Page/s 18-19) to:

Focus more directly on the core findings (low–moderate knowledge, positive attitudes, inconsistent practice, need for training).
Reduce the safety culture discussion to one concise sentence, noting the limited evidence and need for future research.

Reviewer 2 Report

Comments and Suggestions for Authors

REVIEWER REPORT

Manuscript Title: Artificial Intelligence Readiness in Nursing Practice: A Scoping Review of Knowledge, Attitudes, Practice, and Patient Safety Culture

Recommendation: Major Revision

General Comments

This manuscript addresses a highly relevant and rapidly evolving topic: nurses’ readiness for artificial intelligence (AI) implementation in clinical practice. The integration of AI into healthcare systems is accelerating worldwide, and understanding nurses’ knowledge, attitudes, practices, and perceptions regarding AI is essential for safe and effective adoption.

The manuscript is generally well organized, follows a recognized scoping review methodology, and addresses an important gap concerning the relationship between AI readiness and patient safety culture. However, substantial methodological, conceptual, and reporting issues remain. Several inconsistencies between the stated objectives, methods, results, and interpretation raise concerns regarding the robustness of the review. Consequently, I recommend Major Revision before this manuscript can be considered for publication.

MAJOR COMMENTS

Significant inconsistency regarding patient safety culture findings

The central justification for this review is the relationship between AI readiness and patient safety culture. The title, objective, rationale, and research question repeatedly emphasize this relationship.

However, among the ten included studies, only one study actually examined patient safety culture.

This creates a major conceptual imbalance:

The review objective suggests an examination of the relationship between AI readiness and patient safety culture.
The available evidence is insufficient to support meaningful conclusions regarding this relationship.
The final conclusions give the impression that evidence exists when in reality only a single study addresses the topic.

Recommendation:

The authors should substantially revise the title, objectives, and conclusions to reflect the actual evidence base. Alternatively, patient safety culture should be presented as an emerging evidence gap rather than a core review finding.

Inconsistency regarding the main study population

The review aim explicitly focuses on registered nurses.

However, the inclusion criteria allow:

nurses
nursing students
nursing faculty
healthcare workers
healthcare professionals
mixed multidisciplinary samples

Several included studies contain physicians, technicians, administrators, students, and other healthcare workers, making it impossible to isolate nursing-specific findings.

This creates a substantial mismatch between:

review title,
review question,
inclusion criteria,
evidence synthesis.

Recommendation:

Either:

a) restrict the review to nursing populations only,

b) modify the title and objectives to reflect healthcare professionals rather than registered nurses.

Methodological concerns regarding study selection

The PRISMA flow diagram reports:

4,256 identified records
657 duplicates removed
3,599 screened
3,513 excluded
86 full texts retrieved
81 assessed
10 included

The flow diagram contains several inconsistencies.

Specifically:

Five studies are reported as "not retrieved because not reported in English".
Language eligibility should be applied before full-text retrieval and should not be classified as "not retrieved."
Exclusion categories overlap and are not mutually exclusive.

Recommendation:

The PRISMA diagram should be revised according to PRISMA-ScR guidance and eligibility processes should be described more clearly.

Inclusion of a systematic review alongside primary studies

The review includes:

nine cross-sectional studies
one systematic review

Including a systematic review together with primary studies risks double counting evidence and introduces methodological heterogeneity.

The authors do not explain:

why systematic reviews were eligible,
whether overlap between primary studies and the systematic review was assessed,
how duplication of evidence was prevented.

Recommendation:

Provide a clear justification for including systematic reviews and assess potential overlap.

Contradictory interpretation of AI knowledge levels

The manuscript repeatedly states that AI knowledge is generally low to moderate.

However, Table 1 reveals substantial variability:

Some studies report poor knowledge.
Others report satisfactory knowledge.
One Turkish study reports high knowledge levels.
One Saudi study reports good awareness.

The synthesis tends to oversimplify heterogeneous findings.

Recommendation:

Provide a more nuanced interpretation and avoid broad generalizations unsupported by all included studies.

Excessive reliance on descriptive synthesis

The narrative synthesis largely summarizes individual study findings.

There is limited higher-order analytical synthesis.

The review would benefit from stronger thematic integration addressing:

AI literacy
educational preparedness
organizational readiness
ethical concerns
implementation barriers
safety implications

At present, the discussion resembles a sequential description of studies rather than a true scoping review synthesis.

Recommendation:

Develop more analytical themes and explore relationships between concepts.

Discussion relies heavily on secondary citations

Many arguments in the Discussion are supported by studies that were not included in the review.

In several sections, the discussion of external studies exceeds the synthesis of included studies.

This weakens the connection between review findings and conclusions.

Recommendation:

The discussion should be more clearly anchored in the included evidence base.

MINOR COMMENTS

Search period justification

The review includes studies from 2021–2026.

The rationale for selecting this timeframe is not sufficiently justified.

Recommendation:

Explain why studies before 2021 were excluded despite AI in healthcare being actively researched before this period.

Language restriction bias

Only English-language studies were included.

Recommendation:

Discuss the potential impact of language bias in the limitations section.

Geographic concentration of evidence

Most included studies originate from:

Saudi Arabia
China
Bangladesh
Pakistan
UAE
Turkey

Evidence from Europe, North America, Latin America, and Africa is very limited.

Recommendation:

Acknowledge that the findings may not be globally representative.

Table 1 is excessively detailed

The characteristics table occupies multiple pages and contains information that is often repetitive.

Recommendation:

Consider moving detailed psychometric information and JBI scores to supplementary material.

Critical appraisal inconsistency

The manuscript is presented as a scoping review, yet JBI quality scores are calculated and reported for all included studies.

The role of quality appraisal within the synthesis remains unclear.

Recommendation:

Clarify whether methodological quality influenced interpretation of findings.

Terminology should be standardized

The manuscript alternates between:

AI readiness
AI knowledge
AI literacy
AI competence
AI awareness

These concepts are related but not equivalent.

Recommendation:

Provide operational definitions and use terminology consistently.

STRENGTHS

Highly relevant and contemporary topic.
Appropriate use of a scoping review framework.
Comprehensive database coverage.
Inclusion of recent international literature.
Clear presentation of study characteristics.
Identification of important educational and organizational challenges regarding AI implementation.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer #2 Comments

We sincerely thank Reviewer #2 for the detailed and thoughtful feedback. We have carefully revised the manuscript to address all major and minor concerns. Below is our point‑by‑point response.

General Comments: This manuscript addresses a highly relevant and rapidly evolving topic: nurses’ readiness for artificial intelligence (AI) implementation in clinical practice. The integration of AI into healthcare systems is accelerating worldwide, and understanding nurses’ knowledge, attitudes, practices, and perceptions regarding AI is essential for safe and effective adoption. The manuscript is generally well organized, follows a recognized scoping review methodology, and addresses an important gap concerning the relationship between AI readiness and patient safety culture. However, substantial methodological, conceptual, and reporting issues remain. Several inconsistencies between the stated objectives, methods, results, and interpretation raise concerns regarding the robustness of the review. Consequently, I recommend Major Revision before this manuscript can be considered for publication.

Response to General Comments: We appreciate the reviewer’s recognition of the relevance and importance of this topic. We have substantially revised the manuscript to improve methodological clarity, conceptual alignment, and reporting accuracy. All major inconsistencies highlighted by the reviewer have been addressed in detail below.

MAJOR COMMENTS

Major Comment 1: Significant inconsistency regarding patient safety culture findings

However, among the ten included studies, only one study actually examined patient safety culture.

This creates a major conceptual imbalance:

The review objective suggests an examination of the relationship between AI readiness and patient safety culture.
The available evidence is insufficient to support meaningful conclusions regarding this relationship.
The final conclusions give the impression that evidence exists when in reality only a single study addresses the topic.

Recommendation:

Response to Major Comment 1: We agree with the reviewer’s assessment. We have made the following revisions:

Title revised to avoid overstating the relationship between AI readiness and patient safety culture. Revised title: “Artificial Intelligence Readiness in Nursing: A Scoping Review of Knowledge, Attitudes, Practice, and Emerging Evidence on Patient Safety Culture.”
Objectives updated to clarify that patient safety culture is examined as an emerging evidence gap, not a core outcome.
Conclusion rewritten to explicitly state that only one study addressed patient safety culture and that evidence is insufficient to draw firm conclusions.
Discussion revised to frame patient safety culture as a future research priority rather than an established finding.

These changes ensure conceptual alignment between the evidence base and the manuscript’s claims.

Major Comment 2: Inconsistency regarding the main study population

The review aim explicitly focuses on registered nurses.

However, the inclusion criteria allow:

nurses
nursing students
nursing faculty
healthcare workers
healthcare professionals
mixed multidisciplinary samples

Several included studies contain physicians, technicians, administrators, students, and other healthcare workers, making it impossible to isolate nursing-specific findings.

This creates a substantial mismatch between:

review title,
review question,
inclusion criteria,
evidence synthesis.

Recommendation:

Either:

a) restrict the review to nursing populations only,

b) modify the title and objectives to reflect healthcare professionals rather than registered nurses.

Response to Major Comment 2: We appreciate this important observation. To resolve the inconsistency:

We revised the title, aim, and objectives to reflect that the review includes nurses, nursing students, faculty, and other healthcare workers when nurse‑specific data were extractable.
We clarified in the Methods that mixed samples were included only when nurse‑specific findings could be meaningfully interpreted.
We updated the Discussion and Conclusion to avoid implying that findings apply exclusively to registered nurses.

This ensures consistency across the manuscript.

Major Comment 3: Methodological concerns regarding study selection

The PRISMA flow diagram reports:

4,256 identified records
657 duplicates removed
3,599 screened
3,513 excluded
86 full texts retrieved
81 assessed
10 included

The flow diagram contains several inconsistencies.

Specifically:

Five studies are reported as "not retrieved because not reported in English".
Language eligibility should be applied before full-text retrieval and should not be classified as "not retrieved."
Exclusion categories overlap and are not mutually exclusive.

Recommendation:

The PRISMA diagram should be revised according to PRISMA-ScR guidance and eligibility processes should be described more clearly.

Response to Major Comment 3: We have revised the PRISMA‑ScR flow diagram to:

Correctly classify non‑English studies under exclusion criteria rather than “not retrieved.”
Ensure exclusion categories are mutually exclusive and clearly defined.
Align the flow diagram with PRISMA‑ScR reporting standards.

We also clarified the eligibility process in the Methods section.

Major Comment 4: Inclusion of a systematic review alongside primary studies

The review includes:

nine cross-sectional studies
one systematic review

Including a systematic review together with primary studies risks double counting evidence and introduces methodological heterogeneity.

The authors do not explain:

why systematic reviews were eligible,
whether overlap between primary studies and the systematic review was assessed,
how duplication of evidence was prevented.

Recommendation:

Provide a clear justification for including systematic reviews and assess potential overlap.

Response to Major Comment 4: We have added a clear justification in the Methods:

Scoping reviews may include systematic reviews when they provide relevant synthesized evidence.
We assessed the systematic review for overlap and confirmed that none of its included primary studies were part of our dataset.
We clarified that the systematic review was used only to contextualize broader patterns, not to duplicate evidence.

This ensures methodological transparency.

Major Comment 5: Contradictory interpretation of AI knowledge levels

The manuscript repeatedly states that AI knowledge is generally low to moderate.

However, Table 1 reveals substantial variability:

Some studies report poor knowledge.
Others report satisfactory knowledge.
One Turkish study reports high knowledge levels.
One Saudi study reports good awareness.

The synthesis tends to oversimplify heterogeneous findings.

Recommendation:

Provide a more nuanced interpretation and avoid broad generalizations unsupported by all included studies.

Response to Major Comment 5: We revised the Results, Discussion, and Conclusion to provide a more nuanced interpretation:

AI knowledge levels are now described as variable, ranging from low to satisfactory depending on context.
We highlight specific studies reporting higher knowledge (e.g., Turkey, Saudi Arabia).
We avoid broad generalizations and instead emphasize context‑specific variability.

Major Comment 6: Excessive reliance on descriptive synthesis

The narrative synthesis largely summarizes individual study findings.

There is limited higher-order analytical synthesis.

The review would benefit from stronger thematic integration addressing:

AI literacy
educational preparedness
organizational readiness
ethical concerns
implementation barriers
safety implications

At present, the discussion resembles a sequential description of studies rather than a true scoping review synthesis.

Recommendation:

Develop more analytical themes and explore relationships between concepts.

Response to Major Comment 6: We strengthened the analytical synthesis by:

Developing clearer thematic categories:

AI literacy
Educational preparedness
Organizational readiness
Ethical concerns
Implementation barriers
Safety implications

Integrating cross‑study patterns rather than summarizing studies sequentially.
Enhancing conceptual connections between themes.

The revised Discussion now reflects a more robust scoping review synthesis.

Major Comment 7: Discussion relies heavily on secondary citations

Many arguments in the Discussion are supported by studies that were not included in the review.

In several sections, the discussion of external studies exceeds the synthesis of included studies.

This weakens the connection between review findings and conclusions.

Recommendation:

The discussion should be more clearly anchored in the included evidence base.

Response to Major Comment 7: We have revised the Discussion to:

Anchor interpretations primarily in the 10 included studies.
Use external literature only to contextualize findings, not to drive conclusions.
Reduce the number and length of external citations.

This ensures stronger alignment between evidence and interpretation.

MINOR COMMENTS

Minor Comment 1: Search period justification

The review includes studies from 2021–2026.

The rationale for selecting this timeframe is not sufficiently justified.

Recommendation:

Explain why studies before 2021 were excluded despite AI in healthcare being actively researched before this period.

Response to Minor Comment 1: We added a justification explaining that the 2021–2026 timeframe reflects the period of rapid expansion in clinical AI adoption and aligns with recent methodological guidance for contemporary scoping reviews.

Minor Comment 2: Language restriction bias

Only English-language studies were included.

Recommendation:

Discuss the potential impact of language bias in the limitations section.

Response to Minor Comment 2: We added a statement in the Limitations acknowledging potential language bias and its implications for global representativeness.

Minor Comment 3: Geographic concentration of evidence

Most included studies originate from:

Saudi Arabia
China
Bangladesh
Pakistan
UAE
Turkey

Evidence from Europe, North America, Latin America, and Africa is very limited.

Recommendation:

Acknowledge that the findings may not be globally representative.

Response to Minor Comment 3: We expanded the Limitations to note that most included studies originate from Asia and the Middle East, limiting generalizability to other regions.

Minor Comment 4: Table 1 is excessively detailed

The characteristics table occupies multiple pages and contains information that is often repetitive.

Recommendation:

Consider moving detailed psychometric information and JBI scores to supplementary material.

Response to Minor Comment 4: We moved psychometric details and JBI scores to Supplementary Material and streamlined Table 1 for readability.

Minor Comment 5: Critical appraisal inconsistency

The manuscript is presented as a scoping review, yet JBI quality scores are calculated and reported for all included studies.

The role of quality appraisal within the synthesis remains unclear.

Recommendation:

Clarify whether methodological quality influenced interpretation of findings.

Response to Minor Comment 5: We clarified that quality appraisal was conducted for descriptive purposes only, consistent with scoping review methodology, and did not influence study inclusion or weighting.

Minor Comment 6: Terminology should be standardized

The manuscript alternates between:

AI readiness
AI knowledge
AI literacy
AI competence
AI awareness

These concepts are related but not equivalent.

Recommendation:

Provide operational definitions and use terminology consistently.

Response to Minor Comment 6: We added operational definitions for key terms (e.g., AI readiness, AI literacy, AI competence) and standardized terminology throughout the manuscript.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Reviewer Report

Recommendation: Major Revision

General Assessment

This scoping review addresses a timely and important topic: nursing readiness for artificial intelligence, with particular attention to knowledge, attitudes, practice, and the emerging relationship with patient safety culture. The manuscript has several strengths, including searching multiple databases, duplicate independent screening, use of PRISMA-ScR, explicit consideration of organizational readiness and ethical concerns, and an appropriate acknowledgment that the evidence base is predominantly cross-sectional.

However, several methodological and reporting inconsistencies currently undermine confidence in the synthesis. Most importantly, the manuscript’s interpretation of the only included study assessing patient safety culture contradicts the numerical findings reported in Table 1. There are also substantial inconsistencies in study eligibility, PRISMA accounting, critical appraisal, population definition, and the distinction between findings from included studies and additional literature introduced in the Discussion.

1. The interpretation of the only patient-safety-culture study is internally contradictory and must be corrected.

This is the most important issue in the manuscript.

Table 1 reports that Eldesoky et al. found:

AI knowledge: B = 0.296, p < .001;
AI attitude: B = 0.502, p < .001;
model R² = 0.60;

and states that AI knowledge and attitude significantly and positively predicted patient safety culture.

However, the narrative Results subsequently state that the same study produced “mixed and inconclusive findings” and that AI-related factors:

“did not translate into measurable differences in patient safety culture scores.”

The manuscript therefore presents two mutually incompatible interpretations of its only direct study on the principal research question.

Please return to the original Eldesoky et al. article and verify exactly:

which variables were entered into the regression;
whether B values are standardized or unstandardized coefficients;
which outcome was modeled;
whether nurse-specific results were available;
what R² = 0.60 represents; and
whether any practice variable was actually analyzed in relation to patient safety culture.

The Results, Abstract, Discussion, and Conclusion should then accurately reflect those findings.

Because only one study directly addresses this relationship, it would be more appropriate to conclude that one cross-sectional study reported an association, but the evidence base is far too limited to establish consistency, directionality, causality, or generalizability, rather than describing the finding itself as inconclusive if it was statistically positive.

2. The inclusion of a systematic review conflicts with the stated eligibility criteria.

The exclusion criteria explicitly state that studies were excluded if they:

“lacked primary data.”

Yet the manuscript then states that one systematic review was intentionally retained because it provided useful cross-country evidence.

This is methodologically inconsistent.

Please decide whether the review includes:

primary empirical studies only; or
both primary studies and secondary evidence syntheses.

If systematic reviews are eligible, this must be explicitly incorporated into the eligibility criteria and search/synthesis plan. The authors should then explain how double counting was excluded at the primary-study level, not simply state that the systematic review did not overlap with “the original studies included in this review.”

If the primary research question is intended to map empirical KAP and patient-safety evidence, I would favor presenting the systematic review as contextual/background evidence rather than as one of the ten included studies.

3. The PRISMA flow contains numerical and conceptual inconsistencies.

The manuscript states that 86 reports underwent full-text review and that 76 were excluded. However, the listed exclusion reasons are:

AI perceptions: 52
non-healthcare population: 12
nurse data not extractable: 9
insufficient methodological detail: 8
non-English: 5

These sum to 86, not 76.

The figure then reports 86 reports sought, 5 “not retrieved” because they were non-English, 81 assessed for eligibility, and 71 excluded, resulting in 10 included.

These are different classification schemes.

A non-English full-text article is also not a “report not retrieved” if it was retrieved and excluded because of language.

Please reconstruct the PRISMA flow from the screening database and ensure that:

sought for retrieval;
not retrieved;
assessed for eligibility;
excluded at full text; and
reasons for exclusion

are mutually consistent and arithmetically correct.

4. The critical-appraisal methodology requires clarification and likely revision.

The manuscript states that each study was assigned a “JBI critical appraisal score,” and Table 1 reports ratings such as:

8/8 = “high quality”;
7/8 = “good quality”;
6/8 = “moderate–good quality”; and
9/11 = “high quality.”

The manuscript should specify:

the exact JBI checklist used for each study design;
how “Yes,” “No,” “Unclear,” and “Not applicable” responses were converted into numerical scores;
the source or rationale for the thresholds defining “moderate,” “good,” and “high” quality;
who performed the appraisal and whether it was conducted independently by two reviewers.

JBI appraisal tools are generally intended to support criterion-level judgments rather than automatically producing validated global quality categories. If the authors cannot justify these numerical thresholds, I recommend reporting criterion-level appraisal without assigning overall “high/good/moderate” labels.

The systematic review also appears to be scored differently (9/11), so the appraisal approach must be made fully reproducible.

5. The population scope is broader than the title and conclusions imply.

The title refers to “Artificial Intelligence Readiness in Nursing”, and the primary research question explicitly asks about nurses’ KAP.

However, several included primary studies contain mixed samples including physicians, administrative staff, allied health professionals, students, and AI-related professionals.

The eligibility criteria state that mixed studies are eligible when nurse-specific data are “reported or could be meaningfully interpreted.”

“Meaningfully interpreted” is too subjective. Please specify whether nurse-specific quantitative data had to be extractable.

If outcomes were reported only for aggregated multiprofessional samples, they should not be attributed to nurses in the synthesis.

I recommend that Table 1 explicitly identify for each mixed-population study whether:

nurse-specific results were separately reported; or
findings represent the full multidisciplinary sample.

The Abstract and Conclusions should then avoid generalizing multidisciplinary results specifically to nurses.

6. The manuscript overuses predictive and causal terminology for cross-sectional evidence.

The Abstract states that:

“Younger age, higher education, and prior AI exposure predicted more favorable perceptions.”

Most included studies are cross-sectional observational surveys. Unless prospective prediction was assessed, terms such as “predicted” should generally be replaced by “were associated with.”

Likewise, the Discussion states that demographic patterns suggest generational familiarity and educational opportunities “shape readiness.” This should be framed as an interpretation rather than a demonstrated causal mechanism.

The same caution applies to patient safety culture. A cross-sectional association cannot establish that AI knowledge or attitudes “influence” safety culture.

7. The search strategy is insufficiently reproducible in the current manuscript.

The Methods name five databases and summarize the search concepts but do not provide the full executed strategy for each source in the manuscript as presented.

For a review whose search extends through March 2026, please provide:

exact search date for each database;
complete search strings for PubMed, CINAHL, Scopus, Web of Science, and ScienceDirect;
platform used for each database;
date and language limits;
results retrieved from each source;
details of hand-searching/reference-list searching.

The decision to restrict the search to January 2021–March 2026 also requires a clearer methodological justification. “Rapid expansion of AI applications” is plausible but does not establish that relevant nursing AI-readiness studies before 2021 are irrelevant.

If the 2021 cutoff was not protocolized prospectively, this should be reported as a limitation.

8. Conclusions and implications should be calibrated to the actual evidence base.

The manuscript concludes that:

“Strengthening AI literacy, fostering organizational readiness, and explicitly integrating AI into patient safety frameworks will be essential...”

The included evidence can support identifying training needs, positive attitudes, concerns, and perceived organizational gaps, but it cannot establish that particular educational or organizational interventions are “essential” for improving safety.

Similarly, the claim that AI integration “has not yet translated into measurable changes in patient safety culture” is not supported by the review when only one direct study exists—and that study, according to Table 1, actually reports substantial positive associations.

I recommend reformulating the core conclusion along the following lines:

“Available studies suggest generally favorable attitudes toward AI alongside variable knowledge, limited formal training, and persistent ethical and organizational concerns. Evidence linking these factors to patient safety culture remains extremely limited, with only one eligible cross-sectional study identified. Further longitudinal and multisite research is required before the nature or direction of this relationship can be established.”

This would be scientifically stronger and fully aligned with the scope of the review.

Overall Assessment

The topic is relevant, and the manuscript could make a useful contribution by showing that the literature on nursing AI readiness is growing while direct evidence linking AI-related KAP with patient safety culture remains extremely sparse.

However, the current version contains a major internal contradiction regarding the only patient-safety-culture study, inconsistent eligibility criteria, and substantial errors in PRISMA accounting. These are not editorial details; they affect the composition and interpretation of the evidence base.

Author Response

Response to Reviewer #2 Comments (Round 2)

Comment #1. The interpretation of the only patient-safety-culture study is internally contradictory and must be corrected.

This is the most important issue in the manuscript.

Table 1 reports that Eldesoky et al. found:

AI knowledge: B = 0.296, p < .001;
AI attitude: B = 0.502, p < .001;
model R² = 0.60;

and states that AI knowledge and attitude significantly and positively predicted patient safety culture.

However, the narrative Results subsequently state that the same study produced “mixed and inconclusive findings” and that AI-related factors:

“did not translate into measurable differences in patient safety culture scores.”

The manuscript therefore presents two mutually incompatible interpretations of its only direct study on the principal research question.

Please return to the original Eldesoky et al. article and verify exactly:

which variables were entered into the regression;
whether B values are standardized or unstandardized coefficients;
which outcome was modeled;
whether nurse-specific results were available;
what R² = 0.60 represents; and
whether any practice variable was actually analyzed in relation to patient safety culture.

The Results, Abstract, Discussion, and Conclusion should then accurately reflect those findings.

Authors’ Response to Comment #1

Thank you for identifying this critical inconsistency. We carefully re‑examined the original study and confirmed that:

AI knowledge and attitudes were significant positive predictors of patient safety culture.
Attitudes exerted a stronger influence than knowledge.
The model demonstrated substantial explanatory power.

The study’s findings were therefore not inconclusive, but rather indicative of a measurable association.

We revised the Abstract, Results, Discussion, and Conclusion to ensure consistent and accurate interpretation:

Removed all statements describing the findings as “mixed,” “inconclusive,” or “not translating into measurable differences.”
Clarified that one study reported a positive association, but emphasized that the evidence base is extremely limited and cannot support generalizable or causal conclusions.
Updated the Discussion to accurately reflect the study’s contribution while situating it within the broader context of limited international evidence.
Revised the Conclusion to align with the corrected interpretation and highlight the need for further research.

These changes ensure that the manuscript accurately represents the available evidence without overstating its implications.

Below are the specific section revisions throughout the revised version of our paper.

Abstract

We corrected the summary of findings to state that the single included study reported a positive association between AI knowledge, attitudes, and patient safety culture, while noting that evidence remains limited.

Results

The narrative now accurately describes Eldesoky et al.’s findings and removes contradictory language.

Discussion

We replaced prior contradictory statements with a clear explanation that:

The study found a positive association.
Evidence is limited to one setting.
Broader conclusions cannot yet be drawn.

Conclusion

We revised the conclusion to reflect that:

Preliminary evidence suggests a positive association.
The evidence base is too limited to establish consistency or causality.
Further research is needed to explore how AI competencies interact with patient safety culture.

Additional improvements we made based on your Comment #1.

In response to your guidance, we also:

Strengthened transitions to improve coherence.
Ensured consistent terminology across sections.
Removed unnecessary phrasing such as references to “earlier versions of the manuscript.”
Provided tracked‑changes versions of all revised sections for transparency.

Comment #2. The inclusion of a systematic review conflicts with the stated eligibility criteria.

The exclusion criteria explicitly state that studies were excluded if they:

“lacked primary data.”

Yet the manuscript then states that one systematic review was intentionally retained because it provided useful cross-country evidence.

This is methodologically inconsistent.

Please decide whether the review includes:

primary empirical studies only; or
both primary studies and secondary evidence syntheses.

Authors’ Response to Comment #2

We thank the reviewer for this important methodological observation. We agree that the original eligibility criteria created an inconsistency by excluding studies that lacked primary data while later including a systematic review. To address this, we revised the eligibility criteria, study selection description, and related sections to ensure methodological clarity and consistency.

Eligibility Criteria Revised

We updated the eligibility criteria to explicitly state that systematic reviews were eligible for inclusion when they provided synthesized evidence relevant to AI‑related knowledge, attitudes, practice, or patient safety culture. This revision resolves the conflict identified in the original text with the following key revision applied.

Added a statement specifying that systematic reviews are eligible secondary evidence sources.
Modified the exclusion criterion “lacked primary data” to clarify that this applies unless the study is a systematic review contributing synthesized evidence relevant to the review objectives.

Study Selection Section Updated

We revised the Study Selection section to clearly indicate that one systematic review was included as part of the 10 final studies, consistent with the updated eligibility criteria with the following key revision applied.

Added an explicit explanation that the systematic review met the revised eligibility criteria.
Added a methodological clarification describing how double counting was prevented by cross‑checking all primary studies within the systematic review against the empirical studies identified through our database search.

Characteristics of Included Studies Updated

We revised this section to reflect that the final evidence base consisted of nine empirical studies and one systematic review, and added a statement confirming that no overlap existed between the systematic review’s primary studies and the empirical studies included in the scoping review.

Discussion and Conclusion Updated

We added clarifying statements noting that the inclusion of one systematic review further highlights the limited and inconsistent evidence available regarding AI competencies and patient safety culture.

We appreciate the reviewer’s careful attention to methodological detail. The revisions made in response to Comment #2 have strengthened the clarity, coherence, and rigor of the scoping review. Thank you for helping us improve the manuscript.

Comment #3. The PRISMA flow contains numerical and conceptual inconsistencies.

The manuscript states that 86 reports underwent full-text review and that 76 were excluded. However, the listed exclusion reasons are:

AI perceptions: 52
non-healthcare population: 12
nurse data not extractable: 9
insufficient methodological detail: 8
non-English: 5

These sum to 86, not 76.

The figure then reports 86 reports sought, 5 “not retrieved” because they were non-English, 81 assessed for eligibility, and 71 excluded, resulting in 10 included.

These are different classification schemes.

A non-English full-text article is also not a “report not retrieved” if it was retrieved and excluded because of language.

Please reconstruct the PRISMA flow from the screening database and ensure that:

sought for retrieval;
not retrieved;
assessed for eligibility;
excluded at full text; and
reasons for exclusion

are mutually consistent and arithmetically correct.

Authors’ Response to Comment #3

We thank the reviewer for identifying these inconsistencies. We reconstructed the PRISMA flow directly from the screening database to ensure full numerical and conceptual accuracy. The corrected flow now clearly distinguishes between:

reports sought for retrieval,
reports not retrieved,
reports assessed for eligibility, and
reports excluded at full text,

with mutually exclusive categories and correct arithmetic. Non‑English reports were reclassified as “not retrieved” rather than “full‑text exclusions,” resolving the previous overlap. Full‑text exclusion reasons now sum to 71, matching the number of reports assessed for eligibility. We updated both the narrative description and the PRISMA figure accordingly. These corrections ensure complete alignment with PRISMA‑ScR guidelines.

Comment #4. The critical-appraisal methodology requires clarification and likely revision.

The manuscript states that each study was assigned a “JBI critical appraisal score,” and Table 1 reports ratings such as:

8/8 = “high quality”;
7/8 = “good quality”;
6/8 = “moderate–good quality”; and
9/11 = “high quality.”

The manuscript should specify:

the exact JBI checklist used for each study design;
how “Yes,” “No,” “Unclear,” and “Not applicable” responses were converted into numerical scores;
the source or rationale for the thresholds defining “moderate,” “good,” and “high” quality;
who performed the appraisal and whether it was conducted independently by two reviewers.

The systematic review also appears to be scored differently (9/11), so the appraisal approach must be made fully reproducible.

Authors’ Response to Comment #4

We thank the reviewer for this important methodological observation. In response, we have substantially revised the critical‑appraisal section to ensure full alignment with Joanna Briggs Institute (JBI) guidance and to enhance reproducibility.

First, we now explicitly identify the specific JBI checklist used for each study design included in the review (e.g., the JBI Checklist for Analytical Cross‑Sectional Studies for empirical studies and the JBI Checklist for Systematic Reviews and Research Syntheses for the included review).

Second, we have removed numerical scoring (e.g., 8/8, 7/8, 9/11) and eliminated global quality labels (“high,” “good,” “moderate–good”). As the reviewer correctly notes, JBI tools are designed to support criterion‑level appraisal, and numerical thresholds are not validated. Appraisal items are now reported using the standard JBI response categories (“Yes,” “No,” “Unclear,” “Not applicable”) without conversion into aggregate scores.

Third, because numerical thresholds are no longer used, the manuscript no longer requires justification for quality categories. Instead, we present criterion‑level appraisal for each study, consistent with JBI methodology.

Finally, we have added a statement clarifying that two reviewers independently conducted the critical appraisal, with disagreements resolved through discussion or consultation with a third reviewer. This ensures transparency and methodological rigor.

These revisions fully address the reviewer’s concerns and ensure that the appraisal process is clearly described, reproducible, and consistent with JBI standards.

Comment #5. The population scope is broader than the title and conclusions imply.

The title refers to “Artificial Intelligence Readiness in Nursing”, and the primary research question explicitly asks about nurses’ KAP.

However, several included primary studies contain mixed samples including physicians, administrative staff, allied health professionals, students, and AI-related professionals.

The eligibility criteria state that mixed studies are eligible when nurse-specific data are “reported or could be meaningfully interpreted.”

“Meaningfully interpreted” is too subjective. Please specify whether nurse-specific quantitative data had to be extractable.

If outcomes were reported only for aggregated multiprofessional samples, they should not be attributed to nurses in the synthesis.

I recommend that Table 1 explicitly identify for each mixed-population study whether:

nurse-specific results were separately reported; or
findings represent the full multidisciplinary sample.

The Abstract and Conclusions should then avoid generalizing multidisciplinary results specifically to nurses.

Authors’ Response to Comment #5

Thank you for this important observation. We agree that the initial framing of the population scope required clearer alignment with the title and primary research question, which focus specifically on Artificial Intelligence Readiness in Nursing. In response, we have made several substantive revisions to ensure that the synthesis accurately reflects nurse‑specific evidence and does not generalize findings from multidisciplinary samples.

First, we revised the eligibility criteria to remove the subjective phrase “meaningfully interpreted” and now specify that mixed‑population studies were only eligible when nurse‑specific quantitative data were explicitly extractable. Studies reporting only aggregated results for multidisciplinary samples were not used to draw conclusions about nurses and were excluded during full‑text screening when disaggregation was not possible.

Second, Table 1 has been updated to clearly indicate, for each mixed‑population study, whether: nurse‑specific results were separately reported, or findings represent the full multidisciplinary sample. This allows readers to distinguish which results directly reflect nurses’ AI‑related KAP and which reflect broader healthcare groups.

Third, the Abstract and throughout the main text have been revised to ensure that multidisciplinary findings are not attributed to nurses. Where studies included mixed samples, we now explicitly state this and avoid language implying that such results represent nurses unless nurse‑specific data were available.

Finally, the Conclusion has been strengthened to clarify that the evidence base for nurse‑specific AI readiness remains limited, in part because many international studies do not report disaggregated nurse‑specific data. This limitation is now explicitly acknowledged as a methodological constraint and a priority for future research.

Therefore, these revisions ensure that the review’s scope, analysis, and conclusions are fully aligned with the title and primary research question, and that nurse‑specific findings are accurately represented throughout the manuscript.

Comment #6. The manuscript overuses predictive and causal terminology for cross-sectional evidence.

The Abstract states that:

“Younger age, higher education, and prior AI exposure predicted more favorable perceptions.”

The same caution applies to patient safety culture. A cross-sectional association cannot establish that AI knowledge or attitudes “influence” safety culture.

Authors’ Response to Comment #6

We conducted a comprehensive revision of the manuscript to ensure that all interpretations are framed appropriately as associations rather than causal mechanisms.

Abstract Revision

The sentence:

“Younger age, higher education, and prior AI exposure predicted more favorable perceptions.”

has been revised to:

“Younger age, higher education, and prior AI exposure were associated with more favorable perceptions.”

This change removes any implication of temporal prediction or causality.

Discussion Revisions

We revised language suggesting that demographic factors “shape readiness,” which implied causality. The updated sentence now reads:

“Taken together, these demographic patterns suggest that generational familiarity with digital technologies and educational opportunities may be associated with greater readiness for AI adoption.”

This revision frames the interpretation as inferential rather than causal.

Additionally, the opening sentence of the demographic section was refined to remove the em dash and maintain association‑based phrasing:

“The demographic patterns observed in this review, especially the associations among younger age, higher education, and prior exposure to AI, are consistent with findings from multiple international studies.”

Patient Safety Culture Language

We revised all phrasing implying that AI knowledge or attitudes “influence” patient safety culture. For example:

Original:

“AI knowledge and attitudes significantly predicted patient safety culture.”

Revised:

“AI knowledge and attitudes were significantly associated with patient safety culture.”

In the Conclusion, we replaced:

“AI readiness currently influences safety culture…”

with:

“…AI readiness is consistently associated with safety culture…”

These changes ensure that the manuscript does not imply directionality or causality.

Additional Edits throughout the Revised Main Text

To ensure consistency across the manuscript, we systematically replaced causal terminology with association‑based language. These changes ensure methodological accuracy throughout.

Overall, all predictive or causal language has been replaced with association‑based phrasing. Interpretations of demographic patterns are now framed as non‑causal observations. All statements regarding patient safety culture reflect associations rather than influence. Lastly, a manuscript‑wide review ensured consistency and methodological precision. We thank the reviewer for highlighting this issue. These revisions substantially strengthen the scientific rigor and clarity of the revised manuscript.

Comment #7. The search strategy is insufficiently reproducible in the current manuscript.

The Methods name five databases and summarize the search concepts but do not provide the full executed strategy for each source in the manuscript as presented.

For a review whose search extends through March 2026, please provide:

exact search date for each database;
complete search strings for PubMed, CINAHL, Scopus, Web of Science, and ScienceDirect;
platform used for each database;
date and language limits;
results retrieved from each source;
details of hand-searching/reference-list searching.

If the 2021 cutoff was not protocolized prospectively, this should be reported as a limitation.

Authors’ Response to Comment #7

We thank the reviewer for this important and constructive comment. We agree that the search strategy, as originally presented, did not provide sufficient detail to ensure full reproducibility. In response, we have substantially expanded the Methods section and now include all elements requested.

Exact Search Dates, Platforms, and Limits Added

For each of the five major electronic databases—PubMed, CINAHL, Scopus, Web of Science, and ScienceDirect—we now report the exact search dates, platforms used, date limits (January 2021–March 2026), language limits (English), and the number of records retrieved. These details are provided both in the Methods narrative and in a fully reproducible search table.

Complete Executed Search Strings Provided

We have added the full search strings exactly as executed in each database, incorporating controlled vocabulary (e.g., MeSH, CINAHL Headings), free‑text keywords, Boolean operators, truncation, and phrase searching. All search strings now fully align with the concepts described in the Methods section.

Results Retrieved from Each Source

The revised PRISMA flow chart now includes the exact number of records retrieved from each database, consistent with the Study Selection section.

Hand‑Searching and Reference‑List Searching Clarified

We have added a detailed description of hand‑searching procedures. A total of 18 records were identified through hand‑searching, of which 7 were already included from the five electronic databases and therefore did not increase the number of records imported into Covidence.

Justification for the 2021–2026 Date Range

We agree that the rationale for restricting the search to January 2021–March 2026 required clearer justification. We have now added an explanation that this period reflects the rapid expansion of AI‑related nursing research, while also acknowledging that relevant pre‑2021 studies may exist. Because the 2021 cutoff was not prospectively protocolized, this is now explicitly reported as a methodological limitation.

PRISMA‑ScR Alignment

To comply with PRISMA‑ScR guidance, non‑English full‑text reports were classified as full‑text exclusions rather than “not retrieved,” ensuring non‑overlapping exclusion categories.

Supplementary File Submitted

To further enhance transparency and reproducibility, a Supplementary File titled “Complete Search Strategy and Results (January 2021–March 2026)” has been submitted together with the revised version of the manuscript. This supplement contains the full search strategies, database‑specific strings, limits, platforms, search dates, and detailed outputs for all sources.

The revised manuscript now includes complete search strings, database platforms, search dates, limits, retrieved records, hand‑searching details, justification for date limits, and PRISMA‑consistent classification of exclusions. These additions fully address the reviewer’s concerns and substantially improve the transparency and reproducibility of the search strategy.

Comment #8. Conclusions and implications should be calibrated to the actual evidence base.

The manuscript concludes that:

“Strengthening AI literacy, fostering organizational readiness, and explicitly integrating AI into patient safety frameworks will be essential...”

I recommend reformulating the core conclusion along the following lines:

This would be scientifically stronger and fully aligned with the scope of the review.

Authors’ Response to Comment #8

We thank the reviewer for this insightful comment. We agree that the conclusions in the previous version extended beyond what the available evidence can support. In response, we have revised the Conclusions section to ensure that all statements accurately reflect the descriptive nature and limitations of the current evidence base.

Specifically, we removed language implying that particular educational or organizational interventions are “essential” for improving patient safety, as the included studies do not permit causal inference or prescriptive recommendations. We also removed the statement that AI integration “has not yet translated into measurable changes in patient safety culture,” since only one eligible empirical study examined this relationship—and that study reported substantial positive associations.

We have fully adopted the reviewer’s recommended reformulation of the core conclusion. The revised text now states that available studies suggest generally favorable attitudes toward AI alongside variable knowledge, limited formal training, and persistent ethical and organizational concerns. It further clarifies that evidence linking these factors to patient safety culture remains extremely limited, with only one eligible cross‑sectional study identified, and emphasizes that additional longitudinal and multisite research is required before the nature or direction of this relationship can be established. These revisions strengthen the scientific accuracy and calibration of the conclusions and ensure full alignment with the scope and evidentiary boundaries of this scoping review.

Overall Assessment: The topic is relevant, and the manuscript could make a useful contribution by showing that the literature on nursing AI readiness is growing while direct evidence linking AI-related KAP with patient safety culture remains extremely sparse.

Overall Authors’ Response:

We thank very much the reviewer for the thoughtful and constructive overall assessment. We appreciate the recognition that this topic is timely and that the manuscript has the potential to contribute meaningfully by demonstrating both the growth of literature on nursing AI readiness and the scarcity of direct evidence linking AI‑related knowledge, attitudes, and perceptions with patient safety culture.

We fully acknowledge the concerns raised regarding internal inconsistencies, eligibility criteria, and PRISMA accounting. These issues indeed affect the integrity of the evidence base and required substantial correction. In response, we conducted a comprehensive revision of the manuscript to ensure methodological accuracy, transparency, and alignment with scoping‑review standards.

First, we resolved the internal contradiction related to the only patient‑safety‑culture study. We returned to the original article, verified all statistical details, and corrected the interpretation across the Results, Discussion, and Conclusion sections. The revised manuscript now accurately reflects that this single cross‑sectional study reported positive associations, while also emphasizing that one study cannot establish generalizable or causal relationships.

Second, we corrected the eligibility criteria to ensure consistency between the protocol, the Methods, and the included studies. All inclusion and exclusion criteria are now clearly defined and applied uniformly.

Third, we thoroughly reviewed and corrected the PRISMA‑ScR accounting. Duplicate counts, full‑text classifications, and hand‑searching outputs were updated to eliminate overlap and ensure accurate reporting. Non‑English full‑text reports were reclassified appropriately, and the flow diagram now reflects a coherent and reproducible study‑selection process.

Finally, we recalibrated the Conclusions to align strictly with the available evidence. As recommended, we adopted the reviewer’s proposed formulation, stating that available studies show generally favorable attitudes toward AI alongside variable knowledge, limited formal training, and persistent ethical and organizational concerns, while evidence linking these factors to patient safety culture remains extremely limited. We also highlight the need for further longitudinal and multisite research before the nature or direction of this relationship can be established.

We believe these revisions substantially strengthen the methodological rigor, internal coherence, and scientific accuracy of the manuscript, and we are grateful for the reviewer’s guidance in improving the clarity and reliability of the work.

We sincerely hope that we have met the reviewer’s expectations in revising our submission and that the updated manuscript aligns with Nursing Reports’ publication standards. We earnestly hope that these improvements assist in the reviewer’s decision to endorse the acceptance of our submission for publication in Nursing Reports.