Reminiscence functions scale: factorial structure and its relation with mental health in a sample of Spanish older adults
Bibliographic record
Abstract
BACKGROUND: The reminiscence functions scale (RFS) is a 43-item self-report instrument designed to assess the use of reminiscence for different functions. This study aims, on one hand, to analyze the factorial structure and the psychometric properties of the RFS and, on the other, to examine the relationship between the functions of reminiscence and mental health. METHODS: RFS scale and measures of depressive symptomology, despair, and life satisfaction were administered to a sample of persons over the age of sixty (n = 364). RESULTS: After eliminating three conflictive items from the original scale, the confirmatory factor analysis results present a factorial structure comprising eight traditional factors and adequate reliability scores (from 0.73 to 0.87). Using structural equation modeling, we find that these reminiscence factors are organized in three second-order factors (self-positive, self-negative, and prosocial). Results show that the self-positive factor relates negatively and the self-negative factor relates positively with symptoms of mental health problems. CONCLUSIONS: These results, on one hand, confirm that the RFS scale is a useful instrument to assess reminiscence functions in a sample of Spanish older adults and, on the other, that the three-factor model of reminiscence is a better predictor of mental health than the alternative four-factor model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".