Patients with eating disorders. How well are family physicians managing them?
Bibliographic record
Abstract
OBJECTIVE: To assess the attitudes and behaviour of family physicians toward patients with eating disorders (EDs) and to assess these physicians' ongoing learning needs. DESIGN: Confidential survey by mail. SETTING: Family practices in London, Ont. PARTICIPANTS: Two hundred thirty-six general FPs. MAIN OUTCOME MEASURES: Proportion of FPs seeing patients with EDs, screening and management practices, learning needs. RESULTS: Survey response rate was 87.7%; 64% of respondents were male, 36% were female, and 54% had completed a family medicine residency program. Overall, FPs were more comfortable with diagnosis, and less comfortable with management, of EDs. Most respondents shared care with other professionals, usually psychiatrists and nutritionists. Female physicians had identified a larger number of ED patients in their practices and were more likely to screen routinely for EDs. Three quarters of FPs rated their undergraduate training in EDs as poor, and 59% thought their postgraduate training was poor. Outpatient services, diagnostic issues, screening needs, and management planning were identified as important learning needs. Family physicians thought these needs could be best addressed in interactive workshops or peer-led case-discussion groups. CONCLUSION: Family physicians are important in first-line treatment of EDs, but many barriers prevent effective diagnosis and management. Validated screening tools and management strategies could assist FPs in caring for patients with EDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".