Restorative just culture significantly improves stakeholder inclusion, second victim experiences and quality of recommendations in incident responses
Bibliographic record
Abstract
Objective: Matching safety and quality improvements to the complexity of healthcare, Gold Coast Mental Health and Specialist Services implemented a new response to clinical incidents: the Gold Coast Clinical Incident Response Framework (GC-CIRF). It utilises a Restorative Just Culture (RJC) framework and Safety II principles. This paper evaluates its impact.Methods: Staff surveys measured perceptions of just culture and second victim experiences. Quality of recommendations were compared before and after implementation. For the 19 incidents that occurred after the implementation of GC-CIRF, audits of the review processes were undertaken, measuring several components.Results: Results show significant improvement in staff perceptions of just culture and second victim experiences. Review of incident review data showed several shifts in line with Safety II and RJC. The process audit demonstrated inclusion of a broad range of stakeholders, and significant improvements in the quality and strength of recommendations.Conclusions: Embedding RJC and Safety II concepts into the incident review process is associated with improved measures of culture and review outputs. The integration of Safety II concepts and support of cultural shifts will require further work and committed leadership at all levels.
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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 | 0.074 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".