Factors Associated with Performance Among Optometrists in Alberta, Canada:
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".