Development and testing of validity and reliability on frailty assessment scale for elderly inpatients
Bibliographic record
Abstract
ObjectiveTo develop a Frailty Assessment Scale for Elderly Inpatients and conduct a reliability and validity test，providing a scientific and credible tool for clinical nurses to evaluate frailty.MethodsBased on Cumulative deficit model by Canadian Study of Health and Aging（CSHA），combined with literature analysis、Delphi expert consultation、discussion of the research group and presurvey，Frailty Assessment Scale for Elderly Inpatients was preliminarily developed.A survey of 600 elderly inpatients in 4 tertiary A hospital in Shanxi Province was selected for pilot investigation，reliability and validity test and frailty determination of the boundary value.ResultsA scale with 4 dimensions and 19 items was finally determined，the Cronbach's α coefficient of the total scale was 0.934，the test⁃retest reliability was 0.809；content validity was 0.964，the correlation coefficient with FRAIL scale was 0.710，4 factors were extracted through exploratory factor analysis，the cumulative variance contribution rate was 75.902%，confirmatory factor analysis showed fitting degree of factor model was reasonable，and the best boundary value for pre⁃frailty and frailty were determined by ROC curve method to 0.086，0.408.ConclusionFrailty Assessment Scale for Elderly Inpatients constructed in this study has good reliability and validity.It can be used as a tool for evaluating frailty in elderly inpatients.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".