Examining factors associated with job satisfaction among homecare rehabilitation professionals transitioning out of the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
Purpose Homecare rehabilitation professionals (hcRPs) play a critical role in promoting client independence and health management in home and community settings. However, the COVID-19 pandemic has exacerbated burnout, mental health challenges and occupational stress among hcRPs, negatively affecting job satisfaction, care quality and job retention. This study aims to examine factors influencing job satisfaction among Canadian hcRPs transitioning out of the pandemic. Design/methodology/approach This study is part of a larger mixed-methods research project investigating burnout and occupational stress in hcRPs. Quantitative data were collected through self-reported questionnaires from a sample of 100 English-speaking hcRPs employed by a large home care organization. Descriptive analyses were conducted, and two logistic regression models were developed: one analyzing demographic predictors and the other focusing on occupational experiences. Findings Higher levels of social and supervisory support and lower work stress were significantly associated with greater job satisfaction. These results underscore the importance of targeted workplace interventions to enhance social and supervisory support and implement stress-reduction measures. Originality/value This study provides evidence-based insights into the predictors of job satisfaction for hcRPs, an often-overlooked workforce facing unique challenges post-COVID-19. By addressing these factors, organizations can develop effective strategies to improve job satisfaction, enhance care quality and reduce turnover. Future research should investigate causal relationships and the role of job control in hcRPs’ job satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 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".