Advance directives: survey of primary care patients.
Bibliographic record
Abstract
OBJECTIVE: To establish the prevalence of patients with advance directives in a family practice, and to describe patients' perspectives on a family doctor's role in initiating discussions about advance directives. DESIGN: A self-administered patient questionnaire. SETTING: A busy urban family medicine teaching clinic in Hamilton, Ont. PARTICIPANTS: A convenience sample of adult patients attending the clinic over the course of a typical business week. MAIN OUTCOME MEASURES: The prevalence of advance directives in the patient population was determined, and the patients' expectations regarding the role of their family doctors were elucidated. RESULTS: The survey population consisted of 800 participants (a response rate of 72.5%) well distributed across age groups; 19.7% had written advance directives and 43.8% had previously discussed the topic of advance directives, but only 4.3% of these discussions had occurred with family doctors. In 5.7% of cases, a family physician had raised the issue; 72.3% of respondents believed patients should initiate the discussion. Patients who considered advance directives extremely important were significantly more likely to want their family doctors to start the conversation (odds ratio 3.98; P < .05). CONCLUSION: Advance directives were not routinely addressed in the family practice. Most patients preferred to initiate the discussion of advance directives. However, patients who considered the subject extremely important wanted their family doctors to initiate the discussion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".