Development and evaluation of an evidence-based, theory-grounded online Clinical Frailty Scale tutorial
Bibliographic record
Abstract
INTRODUCTION: Frailty is a robust predictor of adverse outcomes in older people. Practice guidelines recommend routine screening for frailty; however, this does not occur regularly. The Clinical Frailty Scale (CFS) is a validated, feasible instrument that can be used in a variety of clinical settings and is associated with many adverse outcomes. Our objective was to develop and evaluate an online training module to guide frailty assessment using the CFS. METHODS: A multidisciplinary team of clinical experts developed an evidence-based, theory-grounded online training module for users who wished to perform frailty assessment using the CFS. The module was prospectively evaluated for user satisfaction, effectiveness and feasibility using a standardised questionnaire. Qualitative feedback was analysed with thematic analysis. RESULTS: Version 1 of the CFS module was used 627 times from 21 October 2019 to 24 March 2020. Satisfaction, effectiveness and feasibility of the module were positively rated (≥4/5 on a 5-point Likert scale n = 582 [93%], n = 507, [81%], n = 575, [91%], respectively). Qualitative feedback highlighted ease of use, likelihood of users to share the module with others and opportunities to increase multimedia content. CONCLUSION: An online tutorial, designed using evidence and theory to guide frailty assessment using the CFS, was positively rated by users. The module's content and structure was rated effective and feasible, and users were satisfied with, and likely to share, the module. Research evaluating the module's impact on the accuracy of frailty assessment is required.
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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 | 0.048 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".