Predictors of Success in a Graduate, Entry-Level Professional Program: From Admissions to Graduation
Bibliographic record
Abstract
INTRODUCTION: Admission to health professional programs (HPPs) in Canada is competitive. The purpose of this study is to evaluate how factors identifiable by the admissions package may predict incidences of academic concerns in one physiotherapy program in Canada. REVIEW OF LITERATURE: Previous literature has identified many concepts that contribute to "academic success." Some HPPs have investigated if admissions criteria can predict students' academic performance. However, this has not been reported in physiotherapy programs in Canada. SUBJECTS: Study data included candidates' admissions' metrics and physiotherapy students' program data for 4 graduating cohorts, who were admitted from 2016 to 2019 inclusive ( N = 256). METHODS: A retrospective, nonconcurrent cohort study was used to estimate the relationship between applicant's admissions data and students' program data pertaining to academic success. Data were summarized as frequencies for categorical variables and means for continuous variables. We calculated odds ratios (ORs) and probabilities of an academic or professional concern for standard scores. Significance was set at P < .05. RESULTS: Cohorts participating in the multiple mini-interview (MMI) had an academic concern incidence of 14/131. The virtual MMI (VMMI) cohort had an incidence of 7/125. Students with higher MMI scores were less likely to have an academic concern (OR = 0.52 [95% CI: 0.30-0.89, P = .017]). Grade point average was not significantly associated with an academic concern when combined with either MMI or VMMI ( P s > 0.05). Admissions round offer was also significantly associated with an academic concern (OR = 2.48 [95% CI: 1.00-6.12, P = .049]), with those beyond the initial round of offers having increased risk of concerns. DISCUSSION AND CONCLUSION: Results of the study reflect the generally low event rates for incidences of academic concerns and the relative homogeneity and range restriction of independent variables across the 4 cohorts of students. HPP's reflection on current admissions processes and ability to identify opportunities for change in admission processes helps ensure that programs are selecting candidates who are likely to succeed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".