Analysis of Available Online Training for Canadian Public Health Professionals
Bibliographic record
Abstract
The aim of this study was to investigate the trends and gaps in online training available for Canadian public health professionals. Our second objective was to compare the available courses to the Skills Online modules offered by the Public Health Agency of Canada. Examining solely Canadian providers, elements collected for each online course included source, cost, type of delivery, duration, language, and accreditation. The seven categories of Core Competencies for Public Health in Canada were matched accordingly to each course based on the available description. A total of 113 online courses were identified, with universities being the leading providers (39%). Two distinct groups of online courses were observed: self-directed and facilitated. Of the 53 self-directed courses, 93% were offered without cost, all could be completed within 5 hours, and none offered university credits for completion. For the 60 facilitated courses, 98% charged a registration fee and 97% required at least 25 hours of time commitment. Facilitated courses primarily administered university credits (56%) or continuing education credits (24%). Additionally, facilitated courses showed a more balanced coverage of the seven core competency categories than self-directed courses. Skills Online offered one of the most comprehensive sets of online courses in public health and was the only provider of facilitated courses in both English and French. Further study is needed to determine the knowledge transfer characteristics of self-directed versus facilitated online training for public health professionals. To improve awareness and access, a database of online Canadian public health courses is recommended.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".