Promises in psychological contract drive commitment for clinicians
Bibliographic record
Abstract
Purpose – Job satisfaction, mental health and organisational commitment are important for clinician retention. Psychological contracts, organisational justice and negative affectivity (NA) have been linked with these outcomes but there is limited research examining these concepts in combination, particularly for clinicians. The aim of this paper is to examine the relationships between psychological contract breach, organisational justice and NA, on the outcomes of organisational commitment, psychological distress and job satisfaction, in a medical context. Design/methodology/approach – Surveys were distributed to Australian hospital clinicians through their internal mail and 81 completed surveys were returned (response rate=24 per cent). Findings – Multiple regression analyses revealed that organisational commitment was related to NA, psychological contract obligation and the interaction between psychological contract breach and distributive justice. Psychological distress was related to NA and procedural justice. Job satisfaction was related to the interaction between psychological contract breach and informational justice, however, the overall model for job satisfaction was not significant. Practical implications – By implementing innovative social exchange processes, healthcare organisations can ensure distributive justice is maintained in the culture in event of contract breach, and by so doing build safety mechanisms into sustaining commitment from clinicians. Originality/value – This paper contributes to the literature on clinical governance in managing the psychological contract to sustain commitment from clinical staff. The findings provide new insights into the factors effecting employee outcomes for clinicians.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".