The SPECTRA study: using co-design to develop a new memory training program for older adults based on the Episodic Specificity Induction
Bibliographic record
Abstract
Abstract Background. Normal aging impacts episodic memory retrieval. Given the crucial role of retrieval for abilities such as future thinking and social problem-solving, the potential of cognitive interventions targeting retrieval extends beyond memory enhancement. Yet only a limited number of such interventions exist. This article outlines the design process of a novel memory training program targeting episodic retrieval. Methods. Using an Intervention Mapping approach, a general outline of the training program was created by leveraging the knowledge base on older adults’ retrieval performance and the principles of the Episodic Specificity Induction (ESI) technique. Two in-person 2-hour co-creation sessions with a focus group of six healthy older adults were conducted in Montréal (Québec, Canada) to develop guidelines for adapting the ESI technique into a training format and to determine tailored user-relevant content. Session recordings and notes were qualitatively analyzed. Training format and content proposed by the focus group were reviewed and approved by the research team. Results. The co-designed training program consisted in six 2-hour sessions provided in small groups of 4 participants. Sessions included supervised ESI administration, complemented by unsupervised ESI self-administration at home. Conclusion. A new program was co-designed to enhance memory retrieval in older adults using the ESI technique. Co-designing the program increases the likelihood that its content and structure respond to users’ challenges and needs, thereby enhancing relevance, engagement, and retention. It holds the potential to generate lasting improvements in retrieval and transfer to crucial cognitive and social abilities.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".