Gendered and racial experiences of moral distress: A scoping review
Bibliographic record
Abstract
AIM: To inform efforts to integrate gender and race into moral distress research, the review investigates if and how gender and racial analyses have been incorporated in such research. DESIGN: Scoping review. METHODS: The PRISMA (Preferred Reporting Items for Systematic and Meta-Analysis) Extension for Scoping Reviews was adopted. DATA SOURCES: Systematic literature search was conducted through PubMed, CINAHL and Web of Science databases. Boolean operators were used to identify moral distress literature which included gender and/or race data and published between 2012 and 2022. RESULTS: After screening and full-text review, 73 articles reporting on original moral distress research were included. Analysis was conducted on how gender and race were incorporated in research and interpretation of moral distress experiences among healthcare professionals. IMPACT: This study found that while there is an upward trend in including gender and race-disaggregated data in moral distress research, over half of such research did not conduct in-depth analysis of such data. Others only highlighted differential experiences such as moral distress levels of women vis-à-vis men. Only about 20% of publications interrogated how experiences of moral distress differed and/or explored factors behind their findings. CONCLUSION: There is a need to not only collect disaggregated data in moral distress research but also engage this data through gender and race-based analysis. Particularly, we highlight the need for intersectional analysis, which can elucidate how social identities and categories (such as gender and race) and structural inequalities (such as those sustained by sexism and racism) interact to influence moral experiences. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Moral distress as experienced by healthcare professionals is increasingly recognized as an important area of research with significant policy implications in the healthcare sector. This study offers insights for nuanced and targeted policy approaches. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".