Development and Validation of Models of Health Professionals' Early and Involuntary Retirement
Bibliographic record
Abstract
Availability of health professionals is fundamental to population health. Multiple trends, including an ageing workforce and selective early retirement, contribute to a shortage of health care providers. PURPOSE: Develop and validate conceptual models of early retirement and involuntary retirement among Registered Nurses (RN) and allied health professionals (AHPs). METHOD: A review of literature relating to early retirement and voluntariness of retirement (n = 22 studies). Any factor reported as predictive of early or involuntary retirement was incorporated into the appropriate conceptual model. To validate the models, we conducted interviews with a diverse group of Canadian RNs and AHPs (n = 14). RESULTS: The conceptual model of early retirement had eight categories including 38 variables: workplace characteristics; sociodemographics; attitudes/beliefs; broader context; organizational factors; family; lifestyle/health, and; work-related. The model of involuntary retirement had three categories (7 variables) : broader context; sociodemographics, lifestyle/health and family. The family category including caregiving responsibilities was added to this model based on interviews. Interview participants reported the models to be clear, logical and relevant. DISCUSSION: RNs and AHPs consider many factors when contemplating retirement; some are sensitive to intercessions by policy-makers and healthcare administrators, which opens up possibilities for those seeking to extend the work lives of older RNs and AHPs. Future testing of operationalized versions of these models will evaluate differences between RN and AHP approaches to retirement decision-making.
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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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".