Risk Factors of Income Inadequacy among Thai Elderly: A National Cross-Sectional Study for 2007 and 2011
Bibliographic record
Abstract
Thailand’s population is aging rapidly. As of 2011, statistics have shown that there has been a constant increase in the percentage of the population aged 60 and older. This study evaluates the causal issues of income deficiency among the elderly in Thailand. The timeline for the study includes two national representative surveys of elderly people, one in 2007 and another in 2011, with double-stage sampling techniques being utilized. The sample is comprised of 30,427 and 34,173 participants in 2011 and 2007, respectively, all aged 60 years and older. SPSS 18 was employed for logistic regression and cross-tabulation analysis. A general decrease in income deficiency was observed in 2011 (38.6%) compared to 2007 (41.9%). The northern region exhibited a higher prevalence of income insufficiency compared to the southern region. Regardless of the prevailing benefit policies for the elderly, the current results demonstrate the need for an augmented government policy that supports elderly individuals facing income deficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".