A Method for Evaluating the Age-friendly Level in Hospitals Based on the Importance and Satisfaction
Bibliographic record
Abstract
Hospital age-friendly design is an important part of the medical security system, and how to evaluate it specifically is of great significance. This paper establishes a complete set of age-friendly methods, and firstly formulates the hospital age-friendly indexes through ergonomics evaluation. Subsequently, the Likert fuzzy semantic scale is used to collect expert opinions, and the independence of the indicators is screened and updated by Pearson correlation test. After that, the updated indicators were assigned importance using the objective CRITIC weighting method. Taking Wuhan Union Hospital as an example, the questionnaire design was used to evaluate the satisfaction of the elderly with each aging indicator of the hospital by using the fuzzy comprehensive evaluation method. Finally, using the BCG Matrix, combined with the importance degree and satisfaction data, it summarizes the aspects of Wuhan Union Medical College that are in urgent need of ageing improvement and the advantages that need to be maintained. This method is universal and can provide important references and improvement suggestions for the aging-friendly design of the hospital, and provide practical care for the actions of the elderly in the hospital, which is of high value.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".