Individual and organizational predictors of allied healthcare providers’ job satisfaction in residential long-term care
Bibliographic record
Abstract
BACKGROUND: Job satisfaction is a predictor of intention to stay and turnover among allied healthcare providers. However, there is limited research examining job satisfaction among allied health professionals, specifically in residential long-term care (LTC) settings. The purpose of this study was to identify factors (demographic, individual, and organizational) that predict job satisfaction among allied healthcare providers in residential LTC. METHODS: We conducted a secondary analysis of data from Phase 2 of the Translating Research in Elder Care program. A total of 334 allied healthcare providers from 77 residential LTC in three Western Canadian provinces were included in the analysis. Generalized estimating equation modeling was used to assess demographics, individual, and organizational context predictors of allied healthcare providers' job satisfaction. We measured job satisfaction using the Michigan Organizational Assessment Questionnaire Job Satisfaction Subscale. RESULTS: Both individual and organizational context variables predicted job satisfaction among allied healthcare providers employed in LTC. Demographic variables did not predict job satisfaction. At the individual level, burnout (cynicism) (β = -.113, p = .001) and the competence subscale of psychological empowerment (β = -.224, p = < .001), were predictive of lower job satisfaction levels while higher scores on the meaning (β = .232, p = .001), self-determination (β = .128, p = .005), and impact (β = .10, p = .014) subscales of psychological empowerment predicted higher job satisfaction. Organizational context variables that predicted job satisfaction included: social capital (β = .158, p = .012), organizational slack-time (β = .096, p = .029), and adequate orientation (β = .088, p = .005). CONCLUSIONS: This study suggests that individual allied healthcare provider and organizational context features are both predictive of allied healthcare provider job satisfaction in residential LTC settings. Unlike demographics and structural characteristics of LTC facilities, all variables identified as important to allied healthcare providers' job satisfaction in this study are potentially modifiable, and therefore amenable to intervention.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".