COMPARISON OF KNOWLEDGE, ATTITUDE, AND PRACTICES TOWARDS CL USAGE AMONGST MEDICAL AND NON-MEDICAL STUDENTS
Bibliographic record
Abstract
Background and Objective: Amidst the myopia epidemic, the number of contact lens (CL) users is steadily increasing, especially youngsters. Many CL practitioners presume optimum knowledge and good practice of CL usage by medical professionals. With rising complications and a significant dropout rate, there is a need to investigate the knowledge (K), attitudes (A), and practices (P) among CL users. This study aimed to assess the K, A, and P in CL users registered in various programs in a university. We hypothesized no difference between K, A, and P in CL users with medical (M) and non-medical (N) academic backgrounds. Methods and Materials: A pre-validated questionnaire, consisting of 19 questions on knowledge, 9 questions on attitude, and 30 questions on practice, was distributed among the university students admitted to various programs, excluding optometry, through an online survey tool. Inferences were drawn using a Chi-square test and Mann-Whitney U test to compare the M and N groups, with a predetermined significance of 0.05. Spearman’s correlation gave a relationship between the K, A, and P of CL users. Results: Out of 1041 (M=146 and N=895) valid responses, 476 (45.7%) respondents had prior experience or were CL users. The prevalence of CL users was a little higher in the N group (M=39.0%; N=46.8%) but statistically not significant (χ2=3.057, df=1, p<0.05). The knowledge about CL hygiene and complications was not significantly different between the two groups (U=10370, z=–1.645, p<0.05). However, there was a significant difference in the attitude towards CL wear and care (U=9268, z= –2.751, p>0.05) and practice of lens wear and care (U=8770, z=–3.318, p>0.05). For all the respondents, a weak correlation between knowledge and practice was observed (r=0.137, p<0.05), and a fair to strong correlation in medical respondents (r=0.465, p<0.05). For the non-medical student group, a statistically significant weak correlation was found between attitude and practice (r=0.110, p<0.05). Conclusion: Medical students exhibited a slightly better attitude and practice toward CL use than non-medical students. However, knowledge scores in both groups were similar. The results of this survey disrupted the presumption that CL users with medical backgrounds possess better K, A, and P toward CL usage. This study highlights the need to be proactive in educating young CL wearers, irrespective of their academic background, providing them with optimum knowledge and skill, and nurturing their right attitude towards CL usage, which will ultimately contribute to the optimization of eye health outcomes and prevention of complications associated with CL wear. This will help the CL industry overcome growth challenges, providing safe opportunities for vision correction to the increasingly prevalent myopic population at large.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".