Prediction of compliance with preventive measures among teachers in the context of the COVID-19 pandemic
Bibliographic record
Abstract
The objective of this study is to examine, in primary and high schools, teachers' compliance with preventive infection control measures (in the context of the COVID-19 pandemic). Inspired by the technology acceptance model (TAM) and occupational health and safety (OHS) literature on personal protective equipment (PPE) use, we propose a model of compliance with preventive measures among teachers. Data were collected following an observational, cross-sectional design. The data for the study were collected via a questionnaire survey of teachers working in the province of Quebec, Canada. To study the impact of the explanatory variables on the dependent variable, we developed a multiple linear regression model. This model was estimated to assess the preventive measures as a whole (six items). Results show that having tested positive for a COVID test in the last year, judging that the situation does not require the use of the mask or the protective glasses, training received on preventive measures, factors related to comfort and use of protective eyewear, as well as age influence teacher compliance with COVID-19 preventive measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".