How do policies promote the sustainable development of older-adult care industry? A configuration analysis based on policy tools
Bibliographic record
Abstract
Background: Under the background of population aging in China, the demand for older-adult care services and products is growing, and the older-adult care industry has great development prospects. A sound older-adult care policy system, that is, an effective policy tool mix, plays an important role in improving the sustainable development of older-adult care industry. Materials and methods: Based on older-adult care policy documents from 31 Chinese provinces, this research extracts older-adult care policy tools via text mining. Then extracted policy tools are taken as conditional variables, and the development of older-adult care industry, which is manifested by the number of older-adult care companies across 31 regions is taken as the result variable. Through applying qualitative comparative analysis, the combined effect of different policy tools on the development of older-adult care industry is obtained. Results and discussions: Results show that a single policy tool cannot constitute the necessary condition to facilitate the older-adult care industry. Hence, policy tools should be applied in combination. Five sustainable policy tool mixes which can promote the development of older-adult care industry are summarized, namely supporting policy-driven mode, fiscal and tax support mode led by supply-oriented policy tools, double-team mode driven by fiscal and tax support and the consumer market, multi-subject joint force mode, and technology compensation mode. The overall findings of this study imply that exploring the policy tool combinations is of vital importance to the sustainable development of older-adult care industry.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".