Giving Back the Pen: Disclosure, Apology and Early Compensation Discussions after Harm in the Healthcare Setting
Bibliographic record
Abstract
In her recently published book After Harm, Nancy Berlinger shares a story about Bishop Desmond Tutu as he comments on the importance of restitution or compensation after an event that has led to harm. Transparency and disclosure are very much on the healthcare agenda in Canada. The increased interest in training providers for difficult conversations and disclosure is a positive sign. Using honest disclosure and apology as important interventions, organizations are beginning to adopt a more open approach to the concept of rebuilding trust after a patient has been harmed. But there continues to be significant reluctance to take the next logical step to solidify the fiduciary relationship between provider and patient - the willingness to enter into early discussions about compensation, non-monetary and otherwise. The Winnipeg Regional Health Authority has developed, with the participation of the facility insurers, a process to identify those cases in which it would be appropriate not only to offer an apology of responsibility but also to initiate discussions around the questions of restitution and compensation. The article describes the steps that led to the development of a detailed process map for such cases and shares the algorithm that has been adopted. As well, the potential challenges associated with such an approach when there are multiple liability and insurance providers are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".