Accounts of Restorative Reconciliation After Medical Injury: Implications for Risk Management
Bibliographic record
Abstract
This research is a qualitative examination of how patients and families describe their long-term recovery in the aftermath of a significant or fatal accidental patient medical injury (PMI). The research is designed to achieve three objectives: (1) explore the continuing lived experiences of those impacted by PMI, (2) advance industry knowledge and management of PMI response, and (3) build on existing voluntary communication-and-resolution program and PMI management infrastructure to activate the potential of CRP discussions, design, and research. We used qualitative data from interviews with 25 of 27 PMI-impacted individuals across the U.S. and Canada to examine acknowledged and existing knowledge gaps pertaining to patient or surviving family member long-term reconciliation and restorative justice. The research concludes by suggesting an emerging theory of ‘restorative reconciliation’ that begins to delineate an actionable process to advance long term non-compensatory recovery from loss, individual healing, and justice after PMI. We do so by focusing on analysis and synthesis of our data, culminating in our initial discovery of a triad of overarching influencers of an individual’s restorative reconciliation after PMI – the influencing factors of understanding, accepting, and redirecting within a spatial recovery dimensions model.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| 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".