Global Mapping of Optometry Workforce
Bibliographic record
Abstract
Abstract BACKGROUND:The growing global burden of vision impairment makes it imperative that there are appropriately trained human resources. Optometrists play a critical role in alleviating this burden, but the low number of optometrists available or the numbers in different countries has always been a challenge for eye care planning. Despite this, there have been limited studies conducted to quantify the deficits in the number of optometrists globally. Methods:A standardised English language questionnaire was used in this cross-sectional study to determine the number and distribution of optometrists globally between February 2017 and May 2020. The survey was translated where necessary and completed by key informants. Using the World Council of Optometry’s scope of practice guidelines, optometrists were defined at levels 2 to 4. Optometrist-to-population ratios were calculated for all countries and regions and compared to targets of 1:50 000 (in developing contexts) or 1:10 000 (in developed contexts).Results:An overall response rate of 80.9% was achieved with responses from 123 of the 152 countries invited. Most (40.7%) key informants were academics. The total number of optometrists across 21 Global Burden of Disease (GBD) regions was 331,781 as of 2019. Sixty-six (53.7%) of 123 countries met the 1:50,000 Optometrist-to-population ratio. A strong (r=0.7) direct positive relationship existed between age-standardised prevalences of blindness and mild- and severe-vision impairment and optometrist-to-population ratios. Strong inverse relationships were observed between country GDP and optometrist-to-population ratio.Conclusion:High-income countries met the target for optometrist-to-patient ratios, while low-to-, middle-income countries and low-income countries did not meet the targets. Low optometrist-to-patient ratios were strongly associated with a higher magnitude of blindness and vision impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".