Evaluation of Reminiscence Therapy on Language Outcomes Among People With Dementia
Bibliographic record
Abstract
Purpose: As people with dementia (PWD) advance in age and disease progression, maintaining cognitive and language skills is essential for overall quality of life. Reminiscence therapy (RT), a promising nonpharmacological intervention, has been shown to maintain and/or improve cognition, agitation, and overall quality of life for PWD. However, preserving language skills, including the ability to effectively make needs and wants known and engage meaningfully in conversations, is also essential for quality of life. There is a paucity of research addressing language skills within RT intervention for PWD. Method: This study included 11 individuals with a diagnosis of dementia who reside in a memory care facility. Participants engaged in two RT treatment cycles (each 8 weeks long and 45 min per session). Measures of cognition (Montreal Cognitive Assessment [MoCA]) and language (Functional Linguistic Communication Inventory–Second Edition [FLCI-2] and discourse questions analyzed using correct information unit [CIU] procedures) were completed at the beginning and end of both RT treatment cycles. Results: There was a significant correlation in MoCA and FLCI-2 measures but no significant correlation between the language measures (CIUs, FLCI-2, or word count). Additionally, as preservation of language skills is imperative, 82% of participants either improved or maintained (within 5 points) FLCI-2 scores and CIU counts (within five CIUs) during intervention. Conclusion: Results from this study suggest that RT could influence the maintenance of language skills in PWD, therefore preserving overall quality of life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".