Knowledge Provision Model Through Distance Learning Method for Promoting Quality of Life of the Elderly in Rural Areas of Thailand
Bibliographic record
Abstract
Thailand is becoming an elderly society like many countries in the world. The number of elderly people is increasing continuously every year. In order to enable the elderly to live with good quality of life in the rapidly changing society, knowledge and information related to their health and living factors are considered to be necessary for them. Therefore, this study was carried out in order to develop a model of knowledge provision for promoting quality of life of the elderly in rural areas of the country. The samples were drawn from every region of the country which included 480 elderly people, 480 elderly caretakers, and 160 people representing the community leaders, community committee members and staff of local government agencies. Both quantitative and qualitative methods were employed for data collection. The study found that there were five areas of knowledge for promoting quality of life of the elderly: physical health, mental health, social relationship, economic, and learning. The model of knowledge provision to the elderly synthesized from the study could enable the elderly to gain necessary knowledge deemed useful for promoting their quality of life. The elderly, the elderly care caretakers and related people were found to be satisfied with the model.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".