Work Disparities and the Health of Nurses in Long-Term Care: A Scoping Review
Bibliographic record
Abstract
Work disparities, such as unfairness in pay or unequal distribution of work experienced by nurses in long-term care (LTC), can impact the retention and health of this workforce. Background: Despite the significant impact of disparities on nurses’ health in LTC, a literature review on work disparities of nurses in LTC has not been conducted. Method: This scoping review aimed to explore the nature and extent of research on meso-level work disparities experienced by nurses in LTC and its links with nurse health and well-being. Five databases were searched: MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), SCOPUS, and CINAHL (EBSCO host). Results: Of the 5652 articles retrieved, 16 studies (14 quantitative and 2 qualitative) published between 1997 and 2024 met the inclusion criteria. A total of 53 work disparities were identified. Only four articles investigated the association of a work disparity with a variable of health (e.g., physical, mental, or poor general health). Conclusions: The results suggest that more attention to how disparities impact nurses’ health and lived experiences is warranted. Meso-level disparities from this review provide an initial basis to consider possibilities in the workplace, especially in supporting equity and opportunities for health and well-being at work (e.g., through fair access to professional growth opportunities and a more equitable balance of work expectations and demands of nursing staff). Future studies of the intersection of macro- and meso-level factors are needed to inform better workplace practices and social and economic policies to support the well-being, health, and safety of nurses at work in LTC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".