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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".