Knowledge about pandemic influenza preparedness among vulnerable migrants in Thailand
Bibliographic record
Abstract
This study was designed to assess factors associated with a high level of knowledge about influenza among displaced persons and labor migrants in Thailand. We conducted a cross-sectional study of 797 documented and undocumented migrants thought to be vulnerable to influenza during the early stages of the 2009 H1N1 pandemic. Data were collected on socio-demographic factors, migration status, health information sources, barriers to accessing public healthcare services and influenza-related knowledge using a 201-item interviewer-assisted questionnaire. Among the different types of influenza, participants' awareness of avian influenza was greatest (81%), followed by H1N1 (78%), human influenza (61%) and pandemic influenza (35%). Logistic regression analyses identified 11 factors that significantly predicted a high level of knowledge about influenza. Six or more years of education completed [odds ratio (OR) 6.89 (95% confidence interval (CI) 3.58-13.24)] and recent participation in an influenza prevention activity [OR 5.27 (95% CI 2.78-9.98)] were the strongest predictors. Recommendations to aid public health efforts toward pandemic mitigation and prevention include increasing accessibility of education options for migrants and increasing frequency and accessibility of influenza prevention activities, such as community outreach and meetings. Future research should seek to identify which influenza prevention activities and education materials are most effective.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".