226 Canadian Family Physician • Le Médecin de famille canadien | Vol 58: FEBRUARY • FÉVRIER 2012 Residents ’ Views | College • Collège Doing more
Bibliographic record
Abstract
In February 2011, I participated in an international elective in Zamboanga City, a large urban centre in the southern Philippines. Throughout my stay, I facili-tated sessions at the Rural Medicine Conference of the Philippines; I taught a module at the Ateneo de Zamboanga University School of Medicine; and I completed a clinical rotation at the Zamboanga City Medical Centre. My pri-mary goal was to learn more about practising family medi-cine in a resource-poor setting. Before arriving, I had read about the complexities of the Philippine health care system. The system is primarily modeled on the American system, while providing free basic health services. To better under-stand the role of family medicine in primary care deliv-ery in the Philippines, I collected qualitative data with an informal questionnaire distributed to several Filipino med-ical students and family medicine residents. From these data, and from my brief personal experience, it appears
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.005 |
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".