Attitudes and Knowledge Concerning Corneal Donation in a Population-Based Sample of Urban Chinese Adults
Bibliographic record
Abstract
PURPOSE: To better understand knowledge and attitudes concerning corneal donation among Chinese adults. METHODS: Randomly selected residents in predetermined age strata 20 to 60+ years completed home-based questionnaires in each of 12 randomly chosen communities in Guangzhou, southern China. RESULTS: Among 1217 selected persons, 430 (35.3%) completed the questionnaires (mean age 40.4 yrs, 57.9% female). Refusers were older (44.8 yrs, P < 0.001), but sex did not differ (52.2% female, P = 0.07). Among participants, 175 (40.7%) were willing to donate their corneas (WTD). Differences between WTD and not WTD included donation knowledge score (range, 1-12) [WTD (SD) 6.91 ± 2.21, not WTD 5.62 ± 2.43, P < 0.001]; having discussed donation (WTD 26.3%, not WTD 8.63%, P < 0.001); viewing donation as unpopular (WTD 88.0%, not WTD 96.5%, P = 0.001); and feeling donation "damages the body" (WTD 15.4%, not WTD 25.7%, P = 0.013). Associated significantly with WTD in multiple regression models were higher knowledge score [odds ratio (OR) = 1.18, 95% confidence interval (CI), 1.04-1.32, P = 0.008]; not feeling donation "damages the body" (OR = 1.91, 95% CI, 1.07-3.43, P = 0.030); and willingness to discuss donation (OR = 10.6, 95% CI, 3.35-33.9, P < 0.001). WTD did not differ by age (>60 yrs: 22/51, 43.1%; ≤60 yrs: 153/379, 40.4%, P = 0.706). Assuming all those refusing the survey would not donate, 14.4% (175/1217) were WTD for themselves, though only 7.1% (86/1217) would do so on behalf of a family member if they did not know the deceased's preference. CONCLUSIONS: Interventions to increase knowledge and promote discussions about donation, and policies allowing widespread expression of donation preference, are needed in this setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".