Memory Support System in Spanish: A Pilot Study
Bibliographic record
Abstract
Subjective cognitive decline (SCD) is prevalent in the general population, particularly among Hispanic adults. SCD increases the risk of mild cognitive impairment (MCI) and dementia. While non-pharmacologic interventions are recommended to mitigate cognitive decline and preserve daily function in SCD and MCI, such interventions are not readily available for Spanish-speaking Hispanic adults with SCD. This pilot study, preregistered at clinicialtrials.gov, aimed to develop a linguistically and culturally appropriate adaptation of an existing memory compensation intervention, the Memory Support System (MSS), from English to Spanish, and to gather data to assess its impact in this population. Twenty Spanish-speaking Hispanic adults with SCD and 16 support partners were recruited. Measures of treatment adherence, daily function, self-efficacy for memory, quality of life, mood, anxiety, and caregiver burden were assessed at baseline, treatment end, and 8-week follow-up. By treatment end, participants with SCD improved their general functional status, daily activities requiring organizational skills, and depression and anxiety symptoms. Partners reported improvement in anxiety by treatment end and in caregiver burden at follow-up. The MSS was successfully translated into Spanish and readily learned by participants with SCD and their partners. The MSS in Spanish may help with daily functioning and aspects of patient and family well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".