Perception of Ophthalmologists of COVID-19 Using the Health Belief Model
Bibliographic record
Abstract
Purpose To assess ophthalmologists' preparedness in such a critical period in the history of pandemics, a logical socio-psychological framework assessment using the health belief model (HBM) is essential to evaluate their risk perception, their willingness to actively participate in engaging in protective health behavior and acknowledge its benefits, and their capability to perform adequate successful methods for limiting the spread of coronavirus disease 2019 (COVID-19) and overcome the barriers they might encounter while implementing such precautions. Methods A cross-sectional study conducted at King Khaled Eye Specialist Hospital using a questionnaire-based (HBM) was distributed to 135 ophthalmologists in the institute to evaluate their risk perception of COVID-19 and determine which components of the HBM contribute to preventive health behavior related to the COVID-19 infection. Results The questionnaire had a reasonable response rate (79.3%, 107 ophthalmologists, including 48 consultants, 51 fellows, and 36 residents). The study demonstrated that this model is useful and mapped how several components were significantly correlated to actions. Most significantly, perceived susceptibility was the most important predictor of action. The second most important determinant of action was the perceived benefit. Conclusion Pandemics such as COVID-19 are likely to happen again in the future. Explicit attention to factors influencing motivation such as threat perception to adopt appropriate health-related behavior to limit the spread of communicable diseases is necessary. This study has successfully represented preparedness and risk behavior perception of ophthalmologists of the novel COVID-19 pandemic in one of the largest tertiary eye hospitals in the Middle East using the Health Belief Model.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".