Pratique clinique Clinical Pract ice
Bibliographic record
Abstract
What role does doctor-patient communica-tion have in relation to legal actions and complaints? In addition to the “technical ” errors complainants claim to have suffered, many also report having faced communication problems.1,2 None of what follows, we should stress, should be taken as an attempt on our part to find ways of preventing patients from taking any recourse to which they are entitled in cases of negligence or error. We should point out an important distinction between complaints related to the treatment process and complaints where there is evidence of negligence or medical error. A physician’s change in attitude might virtually eliminate complaints regarding the treatment process. Better patient-physician communication could no more than blunt the emotional effect on patients of medical errors. Main communication problems reported by patients Studies using various methodologic approaches1-4 have shown that the quality of medical care is not the only thing that determines whether patients take legal action. Relations and communication with physicians—and the dissatisfaction that can result—also have a major role. An estimated 70% to 80 % of medical litigation involves relationship or communication problems. The main sources of dissatisfaction are listed in Table 1. Communication and history of malpractice suits Levinson et al5 were the fi rst researchers to have correlated physicians ’ usual communication strat-egies with their history of malpractice suits. Th eir study is based on tape recordings they made during physicians ’ routine interviews. Table 25 summarizes Dr Lussier is a family physician, and Mr Richard is a psychologist, in Montreal, Que. Complaints and legal actions Role of doctor-patient communication
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.102 | 0.039 |
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".