Reminiscence functions over time: consistency of self functions and variation of prosocial functions
Bibliographic record
Abstract
The current study examines the temporal stability of the tripartite model of reminiscence functions in which eight separate reminiscence functions map onto three second-order factors which contribute significantly to measurement of an overarching reminiscence latent construct. We collected online responses from 411 adults 50+ years of age. Confirmatory factor analytic models were computed at three points of data collection over 16 months. Invariance analyses were next undertaken to simultaneously compare the measurement properties to assess within-person stability of reminiscence functions over time. The tripartite structure of reminiscence functions was replicated at each point of data collection. As hypothesised, self-positive and self-negative functions are consistent across points of data collection, whereas prosocial functions vary over time. The temporal stability of the self functions may be attributed to enduring characteristics of the individual such as personality traits and life attitudes, as well as their solitary nature. Previous research indicates that consistency of self-positive reminiscence functions has ensuing benefits for physical health and psychological well-being; the opposite is true for self-negative functions. The temporal variation of prosocial functions may be due to the varying availability of others to share memories and their responsiveness to the emotional context.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".