Evaluating the Effectiveness of Peer Education Program on Health Knowledge, Attitudes and Practices
Bibliographic record
Abstract
Kelab Doktor Muda (KDM) which translates as Young Doctors Club was established by the Ministry of Health (MOH) Malaysia to train a group of schoolchildren as educators in assisting their peers to adopt healthy practices. This research aims to assess the effectiveness of KDM as a peer education program to increase the health knowledge of school children, and instil healthy practices among schoolchildren by identifying the difference in the level of knowledge, attitude and health practice between school with KDM (SKDM) and school without KDM (NKDM). This is a nationwide comparative cross-sectional study between SKDM and NKDM. This study was carried out between March 2016 and March 2017. Data were obtained using a guided self-administered questionnaire through survey involving 2588 (1294 SKDM; 1294 NKDM) year 5 (11 years old) schoolchildren from 87 primary schools nationwide. The results revealed a significantly higher overall knowledge score of health practices, knowledge and practice score for No Smoking component, as well as hand hygiene knowledge in SKDM compared to NKDM. Schoolchildren in SKDM also had significantly lower BMI compared to NKDM, directly related to healthy eating, active lifestyle and ideal body weight, 3 of the 6 components in this program. As a conclusion, the program was found to be not only effective in increasing the overall knowledge of health practice among schoolchildren, but also results in a meaningful impact to the health of schoolchildren. Further improvements to the modules however should be undertaken to enable more comprehensive changes on the health practices of schoolchildren.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".