Why recall our highs and lows: Relations between memory functions, age, and well-being
Bibliographic record
Abstract
This study examined whether positive and negative memories (life story high and low points) were differentially used for reminiscence functions concerning self and social aspects of reminiscing, and relations between function use and well-being in two age groups. Life story high and low points were collected from a sample of emerging (n =56) and older (n =55) adults, as well as a measure of the use of these memories for the self-functions of death preparation, identity, and problem solving, and the social functions of conversation and teach/inform, and a measure of psychological well-being. Memories were also coded for whether or not they contained a redemptive narrative structure (from emotionally negative to emotionally positive). Results showed that the endorsement of reminiscence functions did differ by memory type, with high points more often endorsed for the functions of identity, teach/inform, and conversation than low points. These main effects were qualified by memory type x age interactions. The use of these functions for each kind of memory was also related to well-being, but differentially for older and younger people, and redemptive sequencing was especially important to the well-being of the younger group. Findings are discussed in terms of the importance of different emotional memories for self and well-being at different points in the lifespan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".