Cross-cultural sdaptation and validation of the Pictorial Fit-Frail Scale (PFFS) for brazilian portuguese
Bibliographic record
Abstract
Introduction: Frailty is a condition that predisposes older adults to adverse outcomes such as institutionalization, hospitalization, and mortality. Detecting and managing frailty are essential in gerontological care. Although numerous diagnostic and screening tools are described in the literature, few can be used directly by patients, their caregivers, or healthcare professionals. One such tool is the Pictorial Fit-Frail Scale (PFFS). Objective: To present the protocol for the cross-cultural adaptation and validation of the PFFS instrument for Brazilian Portuguese. Methods: This study will be conducted in two phases: (1) cross-cultural adaptation and (2) validation. In the content validity phase, nine participants will be included: three older adults, three caregivers, and three healthcare professionals. Statistical analysis will be performed using Finn’s coefficient. In the concurrent validity phase, 141 individuals will participate, distributed equally among older adults, caregivers, and healthcare professionals (47 in each group). The statistical analysis will include Pearson/Spearman correlation tests and Kendall’s rank correlation coefficient. Expected Results and Relevance: As a self-administered tool that can be easily completed by different individuals involved in the care of older adults, including the older adults themselves, its cross-cultural adaptation and validation for the Brazilian context may significantly contribute to comprehensive elderly care and the promotion of healthy aging.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".