5M Strategy for COVID-19 Prevention: A Case Study at Poltekkes Kemenkes Palu
Bibliographic record
Abstract
This study aimed to implement 5M risk communication strategies to prevent COVID-19 at Poltekkes Kemenkes Palu.A cross-sectional design was utilized, and data was collected from 642 participants using a random sampling technique and Google forms distributed through social media.Variables measured included age, gender, status, ethnicity, religion, place of residence, monthly expenses, and risk prevention communication strategies such as wearing masks, washing hands, keeping distance, staying away from crowds, and reducing mobility.The data was analyzed using chi-square tests and binary logistic regression.Results revealed that wearing masks and staying away from crowds were the most significant factors in preventing COVID-19.Participants who never/rarely wore masks were 2.3 times more likely to be infected with COVID-19, while those who never/rarely stayed away from crowds were 2.8 times more likely to be infected.The age group of 40-60 years was identified as being the most at risk, and the study suggests that they should reduce crowds and always wear a mask.In conclusion, this study emphasizes the importance of implementing COVID-19 prevention risk communication at Poltekkes Kemenkes Palu.It provides valuable insights into the significant factors that can reduce the risk of COVID-19 infection, particularly the importance of wearing masks and staying away from crowds.The abstract does not have any major grammatical errors or logical inconsistencies.However, it could be improved by including a brief statement on the practical implications of the study's findings and the potential for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".