Examining Academic Performance Among Pathway and Non-Pathway Health Sciences Students
Bibliographic record
Abstract
Pathway programs providing opportunities for students to more efficiently earn university degrees and college diplomas are proliferating in Canada and internationally. In Ontario, Canada, the University of Ontario Institute of Technology (UOIT) and Durham College (DC) have jointly provided pathway programs for over a decade. These programs, in fields including science, health sciences (allied health sciences, kinesiology, nursing), social science and humanities (legal studies, criminology, commerce), nuclear power, and education (adult education, early childhood studies), facilitate inter-institutional transitions, and enable college graduates to obtain a 4-year (honours) university degree with as little as two additional years of study. This paper provides a quantitative, comparative analysis of the academic performance of pathway students (college-to- university transfer students) and their non-pathway, traditional counterparts (students who enter university directly from secondary school) enrolled in UOIT’s Bachelor of Health Sciences (BHSc) and Bachelor of Allied Health Sciences (BAHSc) programs, and the collaborative UOIT-DC Bachelor of Science in Nursing (BScN) program. Results indicate that pathway students in these health sciences and nursing programs generally outperformed their traditional classmates in overall academic achievement; such results supporting the conclusion that college diploma programs in these areas tend to provide adequate preparation for successful pathway program completion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".