Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it