P1-62 The virtual committee: a practical process for maintaining high quality content of online learning resources for public health practice in Canada
Bibliographic record
Abstract
Introduction The Public Health Agency of Canada's Skills Online program, developed in partnership with government, academic and professional organizations, supports public health core competencies by enhancing the skills of practitioners. This internet-based continuing education program aims to provide relevant, timely and practical Canadian content for public health practitioners. It is delivered in English and French, with an international component. More than 6000 individual public health practitioners have taken at least one of the modules. Subject areas include basic and epidemiology, outbreak management, surveillance, and evidence based public health practice ( http://www.phac-aspc.gc.ca/skills ). A significant challenge for the program is how best to maintain high quality content that reflects the current realities of Canadian and international public health practice, using up-to-date educational approaches, all informed by a variety of public health practitioner experiences. Methods The Public Health Agency of Canada has met this challenge by establishing a multidisciplinary committee, comprised of public health experts, continuing education experts, practitioners and program participants. This “virtual committee” uses a variety of communication options to permit monthly virtual meetings, supplemented by online sessions and demonstrations, and occasional face-to-ace meetings. Results This approach has resulted in a dynamic process for the continued review and revision of the content of online learning modules, establishment of some innovative learning solutions, and ongoing evaluation of the program. Conclusion A virtual committee efficiently and effectively provides for maintenance of content, essential for the Public Health Agency of Canada's continued delivery of high quality continuing education for public health practitioners.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".