The Ethical Management of the Noncompliant Patient
Bibliographic record
Abstract
It is a rare patient who always does everything healthcare providers advise. Sometimes no harm comes from this; sometimes good does. But occasionally, great harm comes from not listening, as when it results in patients returning time and again for costly and invasive treatments of, say, infections, valve replacements, pressure ulcers, and so forth. No class of patients arouses more anger and resentment in healthcare providers, who often put out a call to invoke some version of the three strikes rule and refuse care. And if the patients are also unemployed substance abusers who live in a local park, impolite or dangerous to staff, disruptive to other patients, and have intimidating visitors, the call to say “No” is louder. Can care ever be refused? If so, when? These are the questions we take up in this article. The answers we provide were developed as part of a Paraplegics and Quadriplegics with Pressure Ulcers Project carried out at Vancouver Hospital and Health Sciences Centre. Following an established usage, we refer to patients who exhibit a cluster of the above characteristics, the dominant one of which is a reluctance to heed medical advice, as “noncompliant patients.” This term is offensive to some, but the politically correct lexicon does not provide any alternative which is as short and clear or substantially different. We use the term as a convenient way of referring to a familiar class of patients and without any imputation of blame.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".