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Record W4401409596 · doi:10.1097/jte.0000000000000364

Predictors of Success in a Graduate, Entry-Level Professional Program: From Admissions to Graduation

2024· article· en· W4401409596 on OpenAlex
Gregory F. Spadoni, Sarah Wojkowski, Jenna Smith‐Turchyn, Paul W. Stratford, Lawrence Grierson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Physical Therapy Education · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGraduation (instrument)Medical educationEntry LevelPsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.083
GPT teacher head0.440
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it