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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".