Effect of knowledge and perceptions of risks on Ebola-preventive behaviours in Ghana
Bibliographic record
Abstract
Background: Limited studies exist on the effects of knowledge and risk perceptions in Ebola-preventive behaviours in Ghana. Methods: Using data collected from 800 respondents in 40 randomly selected communities in the Greater Accra Region of Ghana, this study employed hierarchical linear modelling to examine the effects of individual- and community-level factors on Ebola-preventive behaviours. Results: The study found individual- and community-level factors are significantly associated with Ebola-preventive behaviours. Respondents with greater knowledge about Ebola were more likely to engage in Ebola-preventive behaviours. In addition, there were significant changes in risk perception from the time the disease had peaked in neighbouring countries to the time data were collected. Most importantly, respondents who perceived themselves to have a high risk of contracting Ebola at the time of the survey were significantly less likely to engage in Ebola-preventive behaviours. Compared with Christians, Muslims were significantly less likely to engage in Ebola-preventive behaviours. Findings from the multilevel analysis indicated significant differences across communities. Communities expressing worry about a potential Ebola outbreak were more likely to engage in Ebola-preventive behaviours. Conclusion: The findings suggest the importance of adopting behaviour change interventions that address Ebola at both the individual and community level, especially in the event of a future outbreak in Ghana.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".