Pandemic Influenza A (H1N1) and Its Prevention: A Cross Sectional Study on Patients’ Knowledge, Attitude and Practice among l study on Patients’ Knowledge, Attitude and Practice among patients attending Primary Health Care Clinic in Kuala Lumpur, Malaysia
Bibliographic record
Abstract
The World Health Organization confirmed that the novel influenza A, H1N1 as a pandemic on 11 June 2009. After less than three months, 182 countries were affected by the pandemic accounting for about 150,000 infected cases and 3000 mortality. Successful H1N1 pandemic management strategies' shaped by making changes in health behavior. The aim of this study was to document patients' knowledge, attitudes and practices (KAP) regarding the pandemic influenza A (H1N1) and its prevention. We performed a cross-sectional study on knowledge, attitudes and practices (KAP) on preventive measures of Influenza A (H1N1) involving 322 patients attending Klinik Kesihatan Jinjang, a primary health care clinic in Kuala Lumpur, Malaysia from May 10 to 26, 2010 using a face to face interview with a structured pre-tested questionnaire. The majority of the respondents were females (56.8%), Malays (43.2%) aged between 18-27 years old (28.9%). There were significant association between knowledge on the complication of H1N1, effectiveness of the treatment, preventive measures of Influenza A (H1N1) and race (p<0.001) and educational level (p<0.001). There were also significant associations between attitude scores of these patients and their gender (p=0.03), and educational level (p=0.001). Practice scores related to H1N1 were found to be significantly associated with race (p<0.001) and educational level (p<0.001). The significant associations were observed between knowledge and attitude (p<0.001), knowledge and practices (p<0.001), as well as attitude and practices related to H1N1 (p<0.001). Knowledge has a crucial effect on patients' attitude and practice particularly in a pandemic spread. So health policy makers should attempt to disseminate information about preventive measures to community in order to improve their preventive practices during pandemics.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".