Exploration of the promotion path of the smart elderly care model under the background of fewer children in the elderly
Bibliographic record
Abstract
Regarding how to increase the acceptance and promotion scope of the smart elderly care model, this research adopted the questionnaire survey method and randomly selected elderly individuals aged over 60 in Ningbo as the survey subjects. Stratified regression was employed for data analysis. The results indicate that: (1) The majority (68.5%) of the elderly have a relatively high acceptance of companion robots, and this trend decreases with age (B = -0.083, p = 0.048); (2) Openness, initiative, family attitude, community assistance, and corporate donations can all significantly and positively predict the acceptance of companion robots by the elderly. (3) Taking companion robots as an example, the smart elderly care industry has a promising development outlook. In the future, it is necessary to boost the construction of smart elderly care in China from two perspectives: precisely positioning user profiles to develop more age-appropriate intelligent machines and integrating "family-community-enterprise" resources to construct an actor network for the smart elderly care model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".