The employee retention triad in health care: Exploring relationships amongst organisational justice, affective commitment and turnover intention
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To increase understanding of the relationships between organisational justice, affective commitment and turnover intention in health care. BACKGROUND: Turnover in health care is a serious concern, as it contributes to the global nursing shortage and is associated with declines in quality of care, patient safety and patient outcomes. Turnover also impacts care teams and is associated with decreased staff cohesion and morale. METHODS: A survey was developed and administered to frontline nurses working in the Province of Ontario, Canada. The data were used to test a hypothetical model developed from a review of the literature. The relationships amongst the three constructs were evaluated using structural equation modelling and mediation analysis. RESULTS: The hypothesised model was generally supported, although we were limited to considerations of interpersonal justice, affective commitment to one's organisation and turnover intention. Interpersonal justice is associated with affective commitment to one's organisation, which is negatively associated with turnover intention. Interpersonal justice was also found to be directly and negatively associated with turnover intention. Affective commitment to one's organisation was also found to mediate the relationship between interpersonal justice and turnover intention. CONCLUSIONS: The examination of relationships within the "employee retention triad" in a single, comprehensive model is novel and provides new information regarding relational complexity and insights into what healthcare leaders can do to retain employees. RELEVANCE TO CLINICAL PRACTICE: Reducing turnover may help to decrease some of the stressors related to turnover for clinical staff remaining at the organisation such as constant onboarding and orientation of new hires, working with less experienced staff and increased workload due to decreased staffing.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".