Teachers' influence on purchase and wear of children's glasses in rural China: The PRICE study
Bibliographic record
Abstract
IMPORTANCE: Uncorrected refractive error causes 90% of poor vision among Chinese children. BACKGROUND: Little is known about teachers' influence on children's glasses wear. DESIGN: Cohort study. PARTICIPANTS: Children at 138 randomly selected primary schools in Guangdong and Yunnan provinces, China, with uncorrected visual acuity (VA) ≤6/12 in either eye correctable to >6/12 in both eyes, and their teachers. METHODS: Teachers and children underwent VA testing and completed questionnaires about spectacles use and attitudes towards children's vision. MAIN OUTCOME MEASURES: Children's acceptance of free glasses, spectacle purchase and wear. RESULTS: A total of 882 children (mean age 10.6 years, 45.5% boys) and 276 teachers (mean age 37.9 years, 67.8% female) participated. Among teachers, 20.4% (56/275) believed glasses worsened children's vision, 68.4% (188/275) felt eye exercises prevented myopia, 55.0% (151/275) thought children with modest myopia should not wear glasses and 93.1% (256/275) encouraged children to obtain glasses. Teacher factors associated with children's glasses-related behaviour included believing glasses harm children's vision (decreased purchase, univariate model: relative risk [RR] 0.65, 95% CI 0.43, 0.98, P < 0.05); supporting children's classroom glasses wear (increased glasses wear, univariate model: RR 2.20, 95% CI 1.23, 3.95, P < 0.01); and advising children to obtain glasses (increased free glasses acceptance, multivariate model: RR 2.74, 95% CI 1.29, 5.84, P < 0.01; increased wear, univariate model: RR 2.93, 95% CI 1.45, 5.90, P < 0.01), but not teacher's ownership/wear of glasses. CONCLUSIONS AND RELEVANCE: Though teachers had limited knowledge about children's vision, they influenced children's glasses acceptance.
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.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".