Acceptability of the social uses of the COVID-19 screening test among women in southern Benin
Bibliographic record
Abstract
Background: Screening tests are some of the essential measures in the fight against all diseases with epidemic potential. The refusal to use it is the major challenge that hinders this fight. Aim: This article aims to highlight the factors for the rejection of the COVID-19 screening test among women in the informal sector in Benin. Setting: The data were collected in southern Benin. Methods: A cross-sectional approach was used to collect data in two areas (intervention area and buffer zone). The sample was drawn using a two-stage random sampling design. In the first stage, primary sampling units or clusters or villages or neighbourhoods were drawn, and in the second stage, 40 households were selected by primary sampling units. Overall, 2500 households per area in which about 2500 women aged 15-64 years were interviewed. Descriptive and explanatory analyses were carried out. Results: The results show that a strong majority (84.2%) of respondents showed aversion to the COVID-19 screening test. Individual factors (age, level of education, religion) and contextual factors (sectors and types of activities of the respondents) are the main reasons behind this refusal. Conclusion: Insufficient consideration of local contexts around health emergencies, infodemia and social inequalities in health have contributed to aversion to the COVID-19 screening test. Contribution: The results call on public authorities to support a constant improvement of knowledge on COVID-19 taking into account local approaches to facilitate the adherence of populations to the screening test.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".