Two years post-tsunami in Thailand: who still needs assistance?
Bibliographic record
Abstract
BACKGROUND: On 26 December 2004, 280 000 people lost their lives in the Asia-Pacific region. A massive earthquake struck Indonesia, triggering a tsunami that affected several countries, including Thailand. This tsunami had important implications for the health status of Thai citizens and health planning, and thus there is a need to study its long-term impact. METHODS: This cohort study identified determinants of health service utilization (outpatient services, inpatient services, home care, medications and informal care) 1 and 2 years post-tsunami in Thailand. A two-part model with a multivariate logistic regression for each part was used to identify determinants of the propensity and intensity of utilization. RESULTS: Of 1943 participants, 1889 (97.2%) participated at 1 year and 1814/1889 (96.0%) at 2 years. Common determinants of health service utilization in post-tsunami settings were age, marital status, education level, employment status, number of health conditions and (physical and mental) health status. CONCLUSIONS: Knowing the determinants of health service use, health providers may be able to establish programmes for, or to carefully monitor populations, who are more likely to use services. The study results may be used to inform requests for health resources or to assist the development of guidelines for long-term disaster recovery planning.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".