A conceptual framework for defining work disparities: A case of nurses in long term care
Bibliographic record
Abstract
BackgroundIncreasing the recruitment and retention of nurses within long-term care (LTC) is a growing challenge faced by the healthcare community. Addressing this problem will require a greater understanding of the day-to-day experiences of nurses, including the disparities and unequal treatment experienced by this group of workers (e.g., pay parity, discrimination, and unfair job demands). However, while there is a need to better understand work disparities faced by nurses, a formalized definition and framework for examining work disparities do not exist within the literature.ObjectiveTo create a conceptual framework to define and analyze work disparities experienced by nurses in LTC.MethodsThis analysis was conducted in adherence to Podsakoff et al.'s four-stage series of recommendations. A partial survey of the literature and operationalizations of work disparities were analyzed to create a core list of attributes of work disparities among nurses in LTC. A definition and framework for classifying work disparities were then posited through a dialogic process and refined by testing on two studies.ResultsA definition of work disparities was posited and four categories of work disparities were identified: job security, work compensation, work opportunities, and workplace treatment. A matrix for classifying the variables of work disparities and comparator groups was refined.ConclusionWith the increasing recognition of unequal treatment of nurses in LTC, this framework can enable further research within this area to support and enhance opportunities for the retention and health of the LTC workforce.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".