Psychometric development and practical use of questionnaires designed to assess knowledge, attitude, and practice of women regarding the use of sanitizer at home to control coronavirus disease
Bibliographic record
Abstract
Background: This study aimed to develop and practically use a questionnaire to evaluate knowledge, attitude, and practice (KAP) of women regarding the use of sanitizers at home against coronavirus disease 2019 (COVID-19). Methods: An online cross-sectional study was conducted among Iranian women (aged ≥18 years). The KAP items were selected based on the experts’ opinions, and the scale underwent a series of validation processes, including the face, content, and construct validity, and internal consistency for reliability. Results: The internal consistency coefficient exceeded 0.7 for KAP subunits. Exploratory factor analysis (EFA) suggested a three-factor construct for each subunit, and the results of the confirmatory factor analysis (CFA) indicated acceptable fit indices for the proposed models. Overall, 330 women (mean age: 36.78±10.12 years, married: 74.2%, and bachelor’s degree: 46.7%) completed the questionnaire. The level of adequate knowledge on sanitizer use, positive attitude, and good practice achieved were 87.0%, 58.5%, and 66.1%, respectively. Among demographic variables, education level and occupation showed a significant relationship (P<0.05) against KAP and attitude, individually. Conclusion: Despite the high percentage of knowledge, the participants did not get a high attitude and practice score.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".