Translation to Serbian and Transcultural Adaptation of the Fraboni Scale of Ageism in a Population of Community Pharmacists
Bibliographic record
Abstract
The Fraboni Scale of Ageism (FSA) is a comprehensive and explicit instrument for assessing ageism, developed in Canada. However, the effectiveness of its translation into Serbian has not been previously tested. Our aim was to translate the FSA into Serbian, conduct transcultural adaptation, and evaluate its psychometric properties for assessing ageism among community pharmacists. The study adhered to the Pharmacoeconomics and Outcomes Research (ISPOR) guidelines for the translation and transcultural adaptation of questionnaires. The translation was tested on a sample of 436 community pharmacists in Serbia. Reliability was assessed using Cronbach’s alpha, and exploratory factor analysis was conducted using the generalized least squares method with varimax rotation. The final adapted Serbian translation of the FSA consists of 23 questions and demonstrates good reliability, with a Cronbach’s alpha of 0.821. Factor analysis identified seven dimensions of ageism, categorized into cognitive, affective, and behavioral aspects. Factors 1, 4, and 6 (Social Separation of the Elderly, Discrimination Against the Elderly, and Avoidance of Socializing with the Elderly) pertain to the behavioral aspect of ageism. Factors 2 and 3 (Avoidance of Communication with the Elderly and Avoidance of Living with the Elderly) relate to the affective aspect, while Factors 5 and 7 address the cognitive aspect of ageism. The translated and modified FSA scale is a reliable tool for detecting and quantifying ageism among community pharmacists in Serbia.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".