Mapping Out Factors that Undermining Vaccine Uptake in Malaysia: A Multiple Perspective
Bibliographic record
Abstract
INTRODUCTION: Malaysia has recorded a sporadic increase in vaccine-preventable diseases in many different states such as Johor, Perak, Selangor, and Sabah, to name a few. What is more worrying was the drastic drop in vaccination for children especially the measles, mumps, and rubella (MMR) vaccination during the early period of COVID-19 pandemic in 2020. On this basis, this paper is intended to interrogate why vaccine uptake has decreased over the years. When vaccination became a global concern with the surge of COVID-19 cases in the first quarter of 2020, further questions were posed to understand the reality behind vaccine rejections and refusals. MATERIALS AND METHODS: This study employs a focus group discussion and in-depth interviews to explore the vaccine refusal phenomenon in Malaysia. Theoretical sampling led to the recruitment of participants from health institution, media organisation, and vaccine refusal individuals as they are useful to provide different yet connected insights into the phenomenon under study. RESULTS: Under the constructivist-interpretivist paradigm, grounded theory revealed that micro and macro factors jointly contribute to vaccination refusals. CONCLUSIONS: Considering these factors, this study suggests the importance of health literacy and synergised policies to protect, educate, and guide society on vaccine-related matters.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".