The development and psychometric properties of a measure to assess the written submission of an admissions application
Bibliographic record
Abstract
Introduction: Health care programs evaluate prospective applicants using cognitive and non-cognitive criteria. The aim of this research was to develop and evaluate the psychometric properties of a measure to evaluate the non-cognitive criteria of admissions applications. Method: A Masters of Occupational Therapy Written Submission Measure (MOTWSM) was developed and evaluated over 3 phases, using applicants' written statements, resumes, and reference letters. Participants included 50 students who completed an occupational therapy program for determination of internal consistency and test-retest reliability. Additionally, 195 written submissions selected from the applicants who were admitted, waitlisted, and not admitted to the program were evaluated to determine inter-rater reliability using a two-way ANOVA. Analysis of 195 submissions using a one-way ANOVA determined the measure's discriminative validity. Findings: Results indicated test-retest reliability of 0.95 and internal consistency reliability of 0.76. Inter-rater reliability reported a Cronbach's alpha coefficient of 0.86 using a horizontal scoring method. Good discriminative validity was established. Conclusion: The MOTWSM is a reliable and valid measure that can be used to evaluate the non-cognitive criteria of admissions applications in health profession programs. Use of this measure can facilitate selection of the highest caliber of students.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".