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Record W4416867146 · doi:10.15353/cjo.v87i4.6550

Factors Associated with Performance Among Optometrists in Alberta, Canada:

2025· article· W4416867146 on OpenAlexaffvenueabout
Nigel Ashworth, Nicole Kain, Matthew Pietrosanu, Thomas Wilk, Kim Bugera, Homeira Hamayeli-Mehrabani, Nancy Hernández-Cerón, Iryna Hurava, Kusum Kumar

Bibliographic record

VenueCanadian journal of optometry/CJO. Canadian journal of optometry · 2025
Typearticle
Language
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsCollege of Physicians and Surgeons of OntarioUniversity of Alberta
Fundersnot available
KeywordsLogistic regressionGraduation (instrument)CohortCompetence (human resources)LogitMEDLINECohort study

Abstract

fetched live from OpenAlex

Background: Risk and protective factors influencing the performance of health professionals are of significant interest to regulators and the public. We aimed to develop a predictive model to identify factors influencing optometrist performance, providing insights for improving regulatory oversight and supporting targeted interventions.Methods: In our retrospective cohort study, we analyzed data from optometrists registered between 1987 and 2019 in the Alberta College of Optometrists Continuing Competence (CC) program to develop a predictive model for CC practice review outcome. We evaluated reviews using self-assessments, onsite visits, and clinical evaluations, with pass or fail status as the primary outcome. Key covariates included sex, age, training location, and previous review scores. We used a generalized additive model with a logit link and assessed its performance using five-fold cross-validation. Sensitivity and specificity were assessed with a holdout testing set.Results: We analyzed 2,075 CC reviews of 916 optometrists. Of these reviews, 75.6% received a passing grade. Practitioners were primarily male (51.7%, 48.3% female) and trained in the United States (49.8%) or Canada (46.2%). Significant predictors of review outcome were sex, training location, previous review score, follow-up score, age (included as a nonlinear effect varying by sex), and years since last review. In developing a selection tool for future assessments, we replaced age with years since graduation and removed training location. Among the 388 practitioners selected for assessment since 2021, practitioners flagged as high risk had significantly higher failure rates (16.1%) compared with practitioners selected randomly (3.0%).Discussion: Male sex, years since graduation, and poor outcomes on previous reviews emerged as significant predictors of failing an assessment. The developed selection tool effectively identified high-risk practitioners for reassessment, supporting fair and efficient resource allocation in the CC program.Conclusions: Key factors influencing CC review outcomes were identified and a selection tool was developed to ensure fairness across subgroups defined by age and sex.

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.

How this classification was reachedexpand

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)0.010
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0260.022
Science and technology studies0.0020.002
Scholarly communication0.0010.003
Open science0.0040.000
Research integrity0.0020.011
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.366
Teacher spread0.331 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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