INNOVATIONS IN INTERVENTIONS FOR REMINISCENCE AND LIFE REVIEW
Bibliographic record
Abstract
This symposium focuses on innovations in interventions for reminiscence and life review. First, diversity in aging is increasingly recognized in studies on reminiscence and life review. Interventions are discussed that are tailored to specific target groups, like persons with dementia, minority elders, or older adults with depression. Second, societal change plays a role in how interventions are delivered. Given the contemporary focus on self-management in (mental) health, peers play an increasingly important role in supporting each other in reminiscence and life review interventions. Third, technological advancements are taken up in this symposium. New methods to induce autobiographical memories are studied. Innovative information and communication technologies are discussed: digital life story books, online therapy, virtual reality, and smart environments. Fourth, the symposium brings together multiple research methods in assessing how acceptable innovations are for older adults themselves and how effective they are in contributing to lifespan development. Together, the four papers present a design process, from accumulating existing knowledge in a systematic review, the co-creation of interventions in community-based methods, the proof of concept of new technologies in experiments to the evaluation of innovations in randomized controlled trials and interviews. The symposium brings American, Dutch, Spanish, and Canadian researchers together, who are members of the International Institute for Reminiscence and Life Review.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".