Identifying Vulnerable Populations Using a Social Determinants of Health Framework: Analysis of National Survey Data across Six Asia-Pacific Countries
Bibliographic record
Abstract
BACKGROUND: In order to improve the health of the most vulnerable groups in society, the WHO called for research on the multiple and inter-linking factors shaping the social determinants of health (SDH). This paper analyses four key SDH (social cohesion, social inclusion, social empowerment and socioeconomic security) across six Asia-Pacific countries: Australia, Hong Kong, Japan, South Korea, Taiwan, and Thailand. METHODS: Population surveys were undertaken using a validated instrument in 2009-10, with sample sizes around 1000 in each country. The four SDH were analysed using multivariate binomial logistic regression to identify socio-demographic predictors in each country. RESULTS: Low socio-economic security was associated with low income in all six study countries and with poor subjective health in Japan, South Korea and Thailand and with being married or cohabiting in Australia and Hong Kong. Low social cohesion was associated with low income in all countries and with undertaking household duties in South Korea, Thailand and Taiwan. Low social inclusion was associated with low income in Australia, South Korea and Taiwan and with poor subjective health in Australia, Japan and South Korea. Older people had lower social inclusion in Taiwan (50-59 years) and Hong Kong (retired), younger people in Japan and South Korea (20-29 years in both countries) and younger and middle-aged people in Australia. Low social empowerment was associated with low income in Australia, Thailand and Taiwan, with being aged 60 years or over in Australia, Hong Kong and South Korea, and over 50 years in Thailand. CONCLUSIONS: This paper provides baseline measures for identifying where and how policy should be altered to improve the SDH. Furthermore, these data can be used for future policy evaluation to identify whether changes in policy have indeed improved the SDH, particularly for marginalised and vulnerable populations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".