Factors Affecting the Decision Making of HPV Vaccination Uptake Among Female Youth in Klang Valley (Influencing Factors): A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is estimated to affect 500 000 women each year globally, whereby 80% of the cases are in developing nations. Almost all cervical cancer cases were attributed to Human Papilloma Virus (HPV) infection. AIM: To identify factors influencing the decision-making of HPV vaccination uptake as prevention for cervical cancer among female youth in the Klang Valley METHODS: This study used in-depth interview; purposive sampling and snowball sampling method. The questionnaire was based on the Health Belief Model, which consist of perceived susceptibility, severity, benefit, barrier and cues to action. NVivo 7 software was used to process, transcribe and analyse the data from interview sessions. RESULT: This study found that the key driving factors that encouraged female youth to get vaccinated were due to the role of family members and friends, concerns on contracting HPV related illness, free/discounted priced vaccination, recommendation from health care personnel, government’s policy, and benefit (believe in the effectiveness of vaccination). Meanwhile, deterring factors which prevented the uptake of HPV vaccination were lack of knowledge and awareness, costs, healthcare provider and services, time constraint and perceived not at risk. CONCLUSION: Factors leading to the uptake of the HPV vaccine should be seen in a transparent manner to ensure the success of the HPV vaccination program in this country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".