Mea Culpa: Apology Legislation, Accountability and Care
Bibliographic record
Abstract
Abstract Increasingly, jurisdictions are adopting “apology legislation” that allow medical professionals to apologize to patients and family members when an adverse event occurs while disallowing the introduction of the apology in a liability case as evidence of fault or liability. While apology legislation itself is fairly straightforward, its potential meaning and impact is much more complex. This paper conceptualizes apology legislation from an accountability and ethics of care perspective. These two concepts—accountability and care—are distinct but interrelated concepts and this dual theoretical approach offers a rich analysis on the potential impact(s) of apology legislation. We argue that apology legislation is a mechanism added to the existing accountability regime that can offer important opportunities to express and practise care. As an accountability mechanism, apology legislation creates space for an accountability relationship to emerge between medical professionals and their patients. Apology legislation also addresses long-standing gaps in how we as a society think about health care and respond to patients and families in ways that challenge the dominant “consumer of services” role. It is in this sense that apology legislation has the potential to destabilize traditional notions of social citizenship. Last, we argue that empirical research is urgently needed to know to what degree apologies contribute to accountability and the transformation of health care.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".