Examining Online Health Sciences Graduate Programs in Canada
Bibliographic record
Abstract
Approximately one in 10 employed Canadians worked in health care and social services in 2016. Health professionals perceive life-long learning as an important element of professional life and value flexibility in their continuing education activities. Online learning is ideally suited to meet this need for flexible health sciences continuing education. The present study sought to identify and characterize online graduate programs in health sciences offered by Canadian universities. All Canadian (non-technical) university websites were hand searched for online graduate programs in health and related fields. Each identified program was characterized by 10 features: province, university, flexibility (i.e., fully online or blended), subject area, curriculum (e.g., coursework, thesis or project, practicum), duration and timing options (i.e., full-time, part-time), admission requirements, class size and acceptance rates, and employment outcomes. The search identified 171 Canadian university online graduate programs in health and related fields. Across Canada, the greatest numbers of programs are offered in Ontario and British Columbia. Most programs are master’s and graduate certificate programs, with graduate diploma and PhD programs being less common. While the majority of programs require an undergraduate degree for admission, some programs base entry requirements on previous work experience. Most programs offer a blended learning experience, with fewer being fully online. The most common content areas include nursing, public health, occupational health, and occupational therapy. These findings highlight opportunities to advance fully online, health continuing education in novel subject areas.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".