Reminiscence and mental health: a review of recent progress in theory, research and interventions
Bibliographic record
Abstract
ABSTRACT This article explores recent progress in theory, research and practical applications of reminiscence. It first describes the evidence for reminiscence as a naturally occurring process, and discusses the different functions of reminiscence and their relationships with mental health and lifespan processes. Three basic types of reminiscence that relate to mental health are specified: conversations about autobiographical memories and the use of personal recollections to teach and inform others have social functions; positive functions for the self include the integration of memories into identity, recollections of past problem-solving behaviours, and the use of memories to prepare for one's own death; negative functions for the self are the use of past memories to reduce boredom, to revive bitterness, or to maintain intimacy with deceased persons. It is proposed that in interventions the three types are addressed differently: simple reminiscence stimulates social reminiscence and bonding and promotes positive feelings; life review uses the positive functions to enhance personal wellbeing; and life-review therapy seeks to reduce the negative uses and thereby alleviate symptoms of mental illness. Studies of the effectiveness of interventions have provided some evidence that interventions are effective in relation to their goals. The review closes with recommended directions for future reminiscence research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".