Translation, adaptation and pilot testing of the Pictorial Fit-Frail Scale (PFFS) for use in Malaysia – The PFFS-Malay version (PFFS-M)
Bibliographic record
Abstract
BACKGROUND: Frailty is an important health issue in an aging population; it is a state of vulnerability that renders the elderly susceptible to adverse health outcomes, including disability, hospitalization, long-term care admission and death. Early frailty stages are recognizable through screening and are reversible with targeted interventions. To date, however, there is no screening tool for use in Malaysia. The English Pictorial Fit-Frail Scale (PFFS) is a visual tool that assesses a person's fitness-frailty level in 14 health domains, with higher scores indicating higher frailty. OBJECTIVE: The aim was to translate and adapt the English PFFS for use in Malaysian clinical settings. METHODS: The original English PFFS underwent forward and backward-translation by two bilingual translators to and from the Malay language. A finalized version, the PFFS-Malay (PFFS-M), was formed after expert reviewers' consensus and was pilot tested with 20 patients, 20 caregivers, 16 healthcare assistants, 17 nurses and 22 doctors. Score agreement between patients and their caregivers and among healthcare professionals were assessed. All participants rated their understanding of the scale using the feasibility survey forms. RESULTS: A total of 95 participants were included. There were high percentages of scoring agreements among all participants on the scale (66.7% to 98.9%). Overall feedback from all respondents were positive and supported the face validity of the PFFS-M. CONCLUSION: The PFFS-M reflects an accurate translation for the Malaysian population. The scale is usable and feasible and has face validity. Reliability and predictive validity assessments of the PFFS-M are currently underway.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".