INVESTIGATING THE PSYCHOMETRIC PROPERTIES OF THE PICTORIAL FIT-FRAIL SCALE
Bibliographic record
Abstract
The recently developed Pictorial Fit-Frail Scale (PFFS) is an image-based tool that was designed to be easy to administer, sensitive to cultural differences, and a practical approach to identifying frailty compared to other frailty measures. The feasibility, reliability, validity, and responsiveness of the PFFS are currently being tested across three settings: geriatric day hospital (GDH), memory clinic, and primary care clinic. Preliminary analysis was conducted on data from all sites (n=80) to investigate the feasibility (time taken to complete the scale) and inter-rater reliability of the PFFS. Test-rest reliability was conducted on GDH data where patients attended at two time-points (retest within 7–14 days of initial visit; n=42). At all sites, when available, caregivers, nurses, and physicians completed the scale based on the patients’ health. PFFS scores can range from 0–43. A Frailty Index (FI) was constructed based on PFFS scores. Time taken to complete the scale was 4.9 ± 2.4 minutes for patients (n=46), 3.5 ± 1.4 for caregivers (n=43), 1.5 ± 1.1 for nurses (n=72), and 1.9 ± 2.0 for physicians (n=42). Mean patient PFFS score was 15.2 ± 7.0 (FI=0.36 ± 0.16), mean caregiver score was 19.9 ± 8.6 (FI=0.46 ± 0.20), mean nurse score was 15.4 ± 7.5 (FI=0.36 ± 0.18), and mean physician score was 19.2 ± 8.0 (FI=0.44 ± 0.18). There was no significant difference in scores between raters (p>.05). Test-retest reliability was good for patients (ICC=0.70) and nurses (ICC=0.80). Inter-rater reliability between patients/caregivers (ICC=0.83) and nurses/physicians (ICC=0.87) was good. Preliminary findings suggest the PFFS is a feasible and reliable tool, however further analysis is necessary with a larger sample.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".