The Policy Development of Social Welfare for Elderly Health Care in the Community: A Case Study of Phitsanulok Municipality, Thailand
Bibliographic record
Abstract
This research was aimed at developing a social welfare policy on elderly health care in the community through a case study of Phitsanulok Municipality. The objectives of the study were 1) to develop elements and indicators of social welfare for elderly health care, 2) to develop a model for developing social welfare for elderly health care and 3) to investigate the policy development of social welfare for elderly health care. Mixed Method was utilized using survey component analysis research method, content analysis, component confirmation, deep interview and group discussion. The sample groups in this research are 759 elders and 60 organization managers who are involved in social welfare policy for long term elderly health care. Results showed 1) social welfare for elderly health care is included in the existing policy in the area and is operational, but it lacks policy contents which are consistent with the needs of the elderly. Additionally, 2) 34 indicators and five elements were identified as components and parameters of social welfare for the elderly in Phitsanulok. Analysis showed the three elements and six indicators are important and two of the six relate to public health. Analysis by Kaiser-Meyer-Olkin Measure of Sampling Adequacy found KMO value equal to 0.912 and structural reliability α= 0.83-0.97. Lastly, 3) the evaluation results revealed that social welfare policy for elderly health care in Phitsanulok is suitable at a high level.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".