Restorative initiatives: emerging insights from design, implementation and collaboration in five countries
Bibliographic record
Abstract
Introduction: Restorative systems are human centred and distinguished by an emphasis on relational principles and practices. Emerging evidence indicates that a restorative approach holds promise to mitigate and respond to harm in the complex health environment. Advocates are collaborating with clinicians and institutions to develop restorative responses to adverse events. Method: This paper shares the insights of an international network who have been collaborating to nurture the development of restorative policy and practice in five countries since 2019 (Aotearoa New Zealand, Australia [New South Wales & Queensland]; Canada [British Columbia], England and the United States [California]). Our work is at varying stages of maturity and incorporates co-designing, implementing, and evaluating restorative responses to adverse events. Results & discussion: The viewpoint provides an overview of the core principles, emerging evidence, and shares our collective reflections about the constraining and enabling factors to development. We recognise that we cannot speak to the breadth of work underway worldwide. Our hope is that by drawing on our experiences, we can offer some thoughts about what a restorative lens offers the future of patient and family involvement in patient safety, whilst providing the opportunity for transparent critique of work to date.
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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 | 0.135 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".