Nursing home managers’ quality of work life and health outcomes: a pre-pandemic profile over time
Bibliographic record
Abstract
AIM: To examine trends in quality of work life and health outcomes of managers in nursing homes in Western Canada pre-pandemic. METHODS: A repeated cross-sectional descriptive study using data collected in 2014-2015, 2017 and 2019-2020, in the Translating Research in Elder Care Programme. Self-reported measures of demographics, physical/mental health and quality of work life (eg, job satisfaction, burnout, work engagement) were administered and completed by nursing home managers. We used two-way analysis of variance to compare scores across times, controlling for clustering effects at the nursing home level. RESULTS: Samples for data collection times 1, 2, 3, respectively, were 168, 193 and 199. Most nursing home managers were nurses by profession (80.63-81.82%). Job satisfaction scores were high across time (mean=4.42-4.48). The physical (mean=51.53-52.27) and mental (mean=51.66-52.13) status scores were stable over time. Workplace engagement (vigour, dedication and absorption) scores were high and stable over time in all three dimensions. CONCLUSIONS: Nursing home managers were highly satisfied, had high levels of physical and mental health, and generally reported that their work was meaningful over time pre-COVID-19 pandemic. We provided a comparison for future research assessing the impacts of the pandemic on quality of work life and health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".