Admission Factors That Predict Canadian Occupational Therapy Students' Practicum Success
Bibliographic record
Abstract
The evaluation and refinement of admission practices are pertinent topics for admissions committees. There has been limited research that explored the relationship between applicant admission scores and practicum performance. Our study suggests that practicum performance may demand different skills than achieving high academic standing prior to admission. Continued efforts to identify factors predictive of practicum performance will assist occupational therapy (OT) admissions committees to select the highest caliber applicants who will become future practitioners. This study explored which admission factors predicted competency-based fieldwork evaluation for occupational therapists (CBFE-OT) scores for students enrolled in a Canadian Master of Science in Occupational Therapy (MScOT) program. Using a quasi-experimental ex post facto design, 446 admitted MScOT applicants’ scored admission packages, which included their undergraduate grade point average (GPA), two personal statements, a resumé, and two confidential assessment forms (i.e., letters of reference), were analyzed and then correlated with midterm and final CBFE-OT scores across four practicum placements. Confidential assessment forms were also used for separate correlational analyses. Linear regression analyses were completed for significant correlations. Admission package scores were positively correlated with CBFE-OT scores for the final evaluations of students in physical health settings for their fourth practicum placements (p < .05). Alternatively, GPAs were negatively correlated with CBFE-OT scores (p< .05). Admissions practices need to be refined to include salient factors that predict practicum success.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".