Global mapping of optometry workforce
Bibliographic record
Abstract
Background: Vision impairment is a growing global burden issue, and appropriately trained optometrists are essential for its management. However, there is a shortage of optometrists worldwide, which hampers eye care planning. Few studies have addressed this shortage quantitatively. Aim: The study aimed to describe the distribution of the global optometric workforce. Setting: Global and country level. Methods: From February 2017 to May 2020, a standardised questionnaire in English was utilised to collect data on the global number and distribution of optometrists from key informants. Optometrists were categorised based on the World Council of Optometry’s guidelines, from levels two to four. Optometrist-to-population ratios were calculated for all countries and regions and compared with targets of 1:50 000 (in developing contexts) or 1:10 000 (in developed contexts). Results: An 80.9% response 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. Sixty-six (53.7%) countries met the 1:50 000 optometrist-to-population ratio. There was a noticeable positive correlation ( r = 0.7) between the prevalence of blindness and vision impairment and the optometrist-to-population ratios. Strong inverse relationships existed between a country’s gross domestic product 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. Contribution: This article provides the first consolidation of the global optometry workforce.
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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 | 0.003 | 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) | 0.007 | 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".