Working through disclosure and apology with the person and family: a humanizing approach to medical error
Bibliographic record
Abstract
Disclosing and communicating a medical error to a person (as patient) and/or family members can be an overwhelming and difficult conversation. The act of disclosing a medical error in addition to an apology is complex, intense and demanding for the person and family with the healthcare provider because it makes transparent which human or systems error(s), has caused the harm. This manuscript re-interprets narrative data from people (patients) and families who have experienced medical harm through an adaptation of Richard Kearney’s threefold approach to working through human trauma: (1) practical understanding, (2) cathartic narrative and (3) forgiveness (pardon) [1]. Through this analytical approach, the emphasis is on the nature of working through trauma as a dynamic process in the interaction among and between healthcare providers, patients and family members.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".