Supportive Supervision and Staff Intent to Turn Over in Long-Term Care Homes
Bibliographic record
Abstract
Background and Objectives: To examine the association between supervisory support and intent to turn over among personal support workers (PSWs) employed in long-term care (LTC) homes in Ontario, Canada, by assessing whether the association is mediated by job satisfaction and the potential confounding effect of happiness. Research Design and Methods: Cross-sectional survey data of 5,645 PSWs working within 398 LTC homes in Ontario, Canada, were obtained and analysed through a series of multilevel regression models. Results: Overall, analyses support the assertion that the effect of supervisory support on intent to turn over is partially mediated by job satisfaction. However, happiness may act as an effect modifier rather than as a confounder. Discussion and Implications: These results reinforce the importance of supportive supervision for PSWs working in LTC homes and highlight the multifaceted role of nurses in LTC, who traditionally provide the majority of PSW supervision. Nurses must be equipped with competencies and skills that reflect the complex organisational environments in which they work. However, these results must also be interpreted in context with the limitations of cross-sectional data; future research should incorporate prospective data collection and clarify the potential role of happiness.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".