The Development of a New Innovative Online Undergraduate Health Sciences Program: A Case Study
Bibliographic record
Abstract
In September 2016, Queen’s University launched the first, fully online, 4-year Bachelor of Health Science degree program in Canada. This paper reports on the developmental structure, implementation philosophy, and challenges in the development of this competency-based program. All stakeholders directly involved in program development were invited to participate in this qualitative case study. Thirty-five interviews and three focus groups (n=14) were conducted. Interviews and focus groups were transcribed verbatim and data were analyzed using thematic design. Themes included: program vision; desired program outcomes; administrative processes for funding and recruitment; uniqueness of the program; local, regional and international impact of the program; communication and collaborations for program development; and uncertainty in long term outcomes. Findings suggest that during program development, an explicit vision of program goals encouraged buy-in at most levels of the university. There was consensus that the overarching outcome should be to provide a rigorous, high quality program with pathways to professional, basic science, global health and advocacy-based health professions. The online modality was expected to improve accessibility to degree programs, as well as address diverse student learning needs. Innovation played a vital role in the program’s development and was founded in educational theory and curriculum development practices.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".