‘I can't put that on paper.’ How medical professional values shape the content of death certificates
Bibliographic record
Abstract
Abstract This paper follows collegiality, demonstrating how, as a central value of medically trained coroners, it can shape the content of death investigations and certificates. Drawing on ethnographic evidence from a 16-month-long study of the Office of the Chief Coroner (OCC) of Ontario, Canada, I argue that collegiality is an instrument of trust that both affords investigators tremendous access to information, and severely limits the flow of that information into the public domain that the OCC serves. The paper focuses on in-care death investigations, which are those where the OCC's medically qualified coroners find themselves investigating the quality of care delivered by professional colleagues. I show how professional expertise, experience and collegial values often combine to see instances of poor or even incompetent care dealt with privately (rather than publicly) or referred up the medical (rather than public safety) hierarchy. The burden of my argument is that collegial deference to the autonomy and skills of other physicians tends to see coroners expurgate the death certificates they produce. These expurgations obscure competence issues from public view and reduce the accuracy of the certificates. I close with a discussion of the benefits and drawbacks of medically qualified death investigators, as well as potential adjustments to improve the accuracy of in-care death investigations and certifications.
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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 | 0.022 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".