Development and assessment of an educational intervention to improve the recognition of frailty on an acute care respiratory ward
Bibliographic record
Abstract
BACKGROUND: Frailty is a robust predictor of poor outcomes among patients with chronic obstructive pulmonary disease yet is not measured in routine practice. We determined barriers and facilitators to measuring frailty in a hospital setting, designed and implemented a frailty-focused education intervention, and measured accuracy of frailty screening before and after education. METHODS: We conducted a pilot cross-sectional mixed-methods study on an inpatient respiratory ward over 6 months. We recruited registered nurses (RNs) with experience using the Clinical Frailty Scale (CFS). RNs evaluated 10 clinical vignettes and assigned a frailty score using the CFS. A structured frailty-focused education intervention was delivered to small groups. RNs reassigned frailty scores to vignettes 1 week after education. Outcomes included barriers and facilitators to assessing frailty in hospital, and percent agreement of CFS scores between RNs and a gold standard (determined by geriatricians) before and after education. RESULTS: Among 26 RNs, the median (IQR) duration of experience using the CFS was 1.5 (1-4) months. Barriers to assessing frailty included the lack of clinical directives to measure frailty and large acute workloads. Having collateral history from family members was the strongest perceived facilitator for frailty assessment. The median (IQR) percent agreement with the gold-standard frailty score across all cases was 55.8% (47.2%-60.6%) prior to the educational intervention, and 57.2% (44.1%-70.2%) afterwards. The largest increase in agreement occurred in the 'mildly frail' category, 65.4%-81% agreement. CONCLUSIONS: Barriers to assessing frailty in the hospital setting are external to the measurement tool itself. Accuracy of frailty assessment among acute care RNs was low, and frailty-focused rater training may improve accuracy. Subsequent work should focus on health system approaches to empower health providers to assess frailty, and on testing the effectiveness of frailty-focused education in large real-world settings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".