The early retiree divests the workforce: A quantitative analysis of early retirement among health professionals
Bibliographic record
Abstract
Introduction: Availability of health professionals is fundamental to a population's health. Despite shortages of health professionals, we know little about voluntary and involuntary exits from the workforce among publicly-employed Canadian Registered Nurses (RNs) and allied health professionals (AHPs). Limited data on "supply" inhibits the effectiveness of Canadianhealth human resource workforce planning. Early retirement is common among Canadian RNs; data are lacking on AHPs. Purpose: To determine whether publicly-employed Canadian RNs and AHPs differ in their approach to workforce departures between the ages of 45 and 85 years. Objectives: To: 1) develop and validate conceptual models of retirement among RNs and AHPs; 2) identify and compare factors reported to influence retirement decisions among RNs/AHPs; 3) explore the relative importance of factors on early vs. late/"on-time" retirement among RNs/AHPs; 4) quantitatively test conceptual models of early and involuntary retirement among RNs/AHPs; 5) evaluate, comparatively, model fit and association of identified variables with either early or involuntary retirement across occupational groups, and; 6) identify and discuss implications for RN and AHP workforce policy. Methods: To achieve objective 1, I reviewedthe retirement literature (n = 23 studies) and conducted interviews with Canadian RNs/AHPs (n = 14). My source of quantitative data, utilized to achieve objectives 2 through 6, was the Canadian Longitudinal Study on Aging (CLSA). To achieve objectives 2 and 3, I conducted exploratory data analyses (n = 794 RNs and n = 393 AHPs). To achieve objectives 4 and 5, Iconducted logistic regressions for the outcome of early retirement (n = 483 RNs and n = 177 AHPs). To achieve objectives 4 and 5, I conducted a logistic regression for the outcome of involuntary retirement using a combined RN and AHP sample (n = 277). Results: The conceptual model of early retirement had eight categories (38 variables): workplace characteristics; sociodemographics; attitudes/beliefs; broader context; organizational factors; family; lifestyle/health, and; work-related. The model of involuntary retirement had fourcategories (8 variables): broader context; sociodemographics; lifestyle/health and family. Caregiving responsibilities (variable) was added based on interview data. The average age of RN retirement (58.1 years) was significantly lower than that of AHPs (59.4 years). Financial possibility and desire to stop working were among the most frequently reported factors contributing to early and on time/"late" retirement among RNs and AHPs; 85% of RNs and 77% of AHPs retired early. The operationalized model of early retirement explained a maximum of 25% of variance in RN/AHP early retirement. Both RNs and AHPs whose retirement decision had been influenced by organizational restructuring were more likely to have retired early. RNs who felt retirement was financially possible and those with caregiving responsibilities were more likely to retire early. RNs noting a "desire to stop working" as a factor influencing retirement had lower odds of early retirement. Only 8% of variation in involuntary retirement was explained by the tested model. Only self-rated general health and occupation were associated with increased odds of involuntary retirement in a combined sample of RNs and AHPs. Discussion: RNs/AHPs consider many factors when contemplating retirement; some are sensitive to intervention, which generates possibilities for extending RN/AHP work-lives. The prevalence of involuntary retirement among RNs (23%) aligns with national prevalence; only 7% of AHPs reported involuntary retirement. More research is needed to i) deepen our understanding of publicly employed RN/AHP pathways to early and involuntary retirement, and ii) understand the reasons for differences in RN and AHP pathways to retirement. Conclusion: There is much to learn about publicly-employed RN and AHP pathways to retirement. The models tested in this studyhad much greater explanatory power for early retirement than involuntary retirement (25 vs 8% explained variance) suggesting that much is unknown regarding determinants of involuntaryretirement. The conceptual models have only been partially tested – further quantitative testing is needed; such testing requires a larger sample of RNs and AHPs and the inclusion of work-related variables. Potential strategies to reduce the rate of early retirement may include: reducing the frequency of restructuring in healthcare and improving its' implementation; legislation to expand paid leave policies to those providing informal care, and; subsidization of caregiving support forwould-be caregivers wishing to remain in the workforce. Work-based interventions that improve self-rated health may reduce the rate of involuntary retirement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".