Financial Inequities in Optometric Education in Canada: A Comparison of Two Optometry Programs
Bibliographic record
Abstract
SIGNIFICANCE: The advancing age of the population will require increased access to eye care services to manage eye diseases and vision correction. Optometric education requires a sound financial plan to manage student debt. This study evaluates the financial inequalities of optometric programs in Canada and how this may impact the provision of eye care professionals. PURPOSE: The objective of this study was to compare the financial inequities in optometric education in Canada from the 2020 graduating class. METHODS: A cross-sectional study assessed monetary variables related to the study of optometry in Canada, including academic and personal expenses, and overall debt and expenses related to the COVID-19 lockdown for the 2020 graduating class. RESULTS: A total of 108 optometry students from the 2020 graduating classes of the University of Montreal and the University of Waterloo responded, with 68 (female/male respondents, 53:15; mean [standard deviation] age, 25.66 [2.01] years) completing the study. Waterloo students spent more years in university ( P < .001), had higher academic fees ( P < .001), spent more on traveling to their family residence ( P = .007), and received more provincial ( P = .002) and federal ( P < .001) loans than Montreal students. Overall debt before optometry was similar among students but differed ( P < .001) at the end of their program, with Waterloo students having a higher debt burden. CONCLUSIONS: There is a financial inequity in optometric education in Canada depending on the chosen program. Cumulative optometry student debt for the 2020 graduating class in Canada ranges from Can $0 to $189,000 with an average of Can $65,800 and a median of Can $50,000. The results of this study can assist financial and government agencies, and future optometry students to better understand the financial burdens and establish a financial plan to study optometry in Canada, to respond to the growing eye care needs of the public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".