Enhancing Undergraduate Public Health Education Through Public Health Interest Groups
Bibliographic record
Abstract
PURPOSE: Since 2007, all Canadian medical schools have had at least one established student-led public health interest group (PHIG). The Association of Faculties of Medicine of Canada (AFMC), the Public Health Task Group, and the Public Health Agency of Canada (PHAC) have supported these PHIGs. The authors describe the activities and structure of PHIGs in Canada from 2007 to 2011, plus the extent to which PHIGs met the objectives set out for them by the AFMC Public Health Task Group. METHOD: Using a standardized template, the authors analyzed funding applications and reports that PHIG executives submitted to the AFMC from 2007 through 2011. The authors created activity categories and collected simple counts of activities within categories. They then used these data to assess how successfully PHIGs have been able to meet their objectives. RESULTS: Fifty-two funding applications, 50 interim reports, and 48 final reports were available for analysis. All 17 Canadian medical schools had at least one established PHIG between 2007 and 2011, and 9 schools (53%) applied for PHIG funding in all four years. Academic activities such as lectures, seminars, and workshops were the most common activities conducted by PHIGs, followed by career exploration and networking. CONCLUSIONS: This study found that the AFMC, with funding support from PHAC, was instrumental in initiating PHIGs in 82% (n = 14) of Canadian medical schools. With consistent funding, national networking opportunities, and a common operating structure, PHIGs have been able to accomplish AFMC's objectives for increasing public health awareness amongst medical students.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".