Development and validation of frailty and malnutrition knowledge assessment scale for community-dwelling older adults
Bibliographic record
Abstract
There is a lack of reliable tools to assess the knowledge of frailty and malnutrition in community-dwelling older adults. To develop and validate reliable frailty and malnutrition knowledge assessment scales for this population, two scales were developed and validated through five phases. Phase 1: the item pools were constructed through a literature review and research panel based on the symptom interpretation model. Phase 2: the expert consultation was performed to select the items. Phase 3: a pilot survey was conducted to assess the clarity of the items and further revise the scales. Phase 4: 242 older adults were surveyed to finalize the items. Phase 5: 241 older adults were surveyed to test the psychometric properties. The two scales each comprise 3 dimensions (symptoms, risk factors, and management strategies) and 11 items. They had good construct validity, with all indicators of correlation analysis and confirmatory factor analysis meeting their specific criteria. The reliability of the frailty and malnutrition knowledge assessment scales was good, with composite reliability coefficients all >0.60, Cronbach's alpha being 0.81 and 0.83, and the Spearman-Brown coefficient being 0.74 and 0.80, respectively. Their acceptability was good, with both having a completion rate of 92.18% and an average completion time of 3 min. The two scales are reliable tools to assess the knowledge of frailty and malnutrition among community-dwelling older adults, especially for large-scale surveys. They can help identify knowledge gaps in older adults and provide a basis for developing targeted educational interventions.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".