Humanizing harm: Using a restorative approach to heal and learn from adverse events
Bibliographic record
Abstract
BACKGROUND: Healthcare is not without risk. Despite two decades of policy focus and improvement efforts, the global incidence of harm remains stubbornly persistent, with estimates suggesting that 10% of hospital patients are affected by adverse events. METHODS: We explore how current investigative responses can compound the harm for all those affected-patients, families, health professionals and organizations-by neglecting to appreciate and respond to the human impacts. We suggest that the risk of compounded harm may be reduced when investigations respond to the need for healing alongside system learning, with the former having been consistently neglected. DISCUSSION: We argue that incident responses must be conceived within a relational as well as a regulatory framework, and that this-a restorative approach-has the potential to radically shift the focus, conduct and outcomes of investigative processes. CONCLUSION: The identification of the preconditions and mechanisms that enable the success of restorative approaches in global health systems and legal contexts is required if their demonstrated potential is to be realized on a larger scale. The policy must be co-created by all those who will be affected by reforms and be guided by restorative principles. PATIENT OR PUBLIC CONTRIBUTION: This viewpoint represents an international collaboration between a clinician academic, safety scientist and harmed patient and family members. The paper incorporates key findings and definitions from New Zealand's restorative response to surgical mesh harm, which was co-designed with patient advocates, academics and 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 | 0.077 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.116 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".