Physician do not heal thyself. Survey of personal health practices among medical residents.
Bibliographic record
Abstract
OBJECTIVE: To assess how many residents follow the recommendation that physicians have a personal family physician and where residents seek medical attention when needed. DESIGN: Hand-delivered survey. SETTING Residency training programs at Queen's University. PARTICIPANTS: Of 215 residents with a central mailbox, 122 responded (response rate 57%). MAIN OUTCOME MEASURES: Health status, usual access to health care, having a personal family physician, and response to two scenarios. RESULTS: More than a third (38%) of residents have a local family physician, yet 25% of those with chronic illnesses and 40% of those who use prescription medications regularly do not. Many rely on colleagues; 41% have received prescriptions from or written prescriptions for their colleagues. Residents with local family physicians are more likely to seek appropriate medical attention for physical problems. Residents do not recognize or seek treatment for mental health problems. Knowledge, time, and accessibility were considered barriers to adequate health care. CONCLUSION: Many residents do not have good access to comprehensive, confidential, and objective medical care. They rely on colleagues, and they ignore mental health problems. Lack of time and access, and attitudes about the importance of having a family physician are important barriers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".