FEASIBILITY AND ACCEPTABILITY OF A TELEHEALTH-BASED GROUP INTERVENTION FOR VETERANS WITH MCI
Bibliographic record
Abstract
Abstract MInds Navigating the Diagnosis of Mild Cognitive Impairment (MiND-MCI) is a nine-session telehealth intervention developed for older Veterans experiencing stress related to MCI. MIND-MCI targets memory and thinking abilities, stress/coping, and lifestyle risk factors through peer support, education, and skills training. This DoD-funded study aims to evaluate the feasibility, acceptability, and preliminary effectiveness of MIND-MCI to improve QoL among Veterans with MCI. At baseline, participants (n = 16) in this pilot study were ages 61-79 years old and scored in the mild cognitive impairment range (M = 22.8) on the Montreal Cognitive Assessment. Retention rates (88%) and attendance (M = 7/9 sessions) of study completers suggest feasibility and acceptability of the intervention. Eleven participants demonstrated high engagement (attended ≥ 75% of sessions). Participants reported a high level of acceptability (120/147 total score possible) on the Telehealth Usability Questionnaire, and a majority of participants indicated that they agree or completely agree with the acceptability of the intervention on two additional measures (Acceptability of Intervention Measure and the Intervention Appropriateness Measure). Preliminary feedback was obtained from study interventionists to identify barriers and facilitators for delivering this treatment over telehealth. Themes included: range of severity of MCI across participants, session interruptions and delays related to troubleshooting technological issues, strategies used to support older adults’ use of a telehealth platform, and the importance of peer support. Preliminary results support strong engagement with and acceptability of telehealth modalities in this older adult population, a population often presumed to lack telehealth capability and proficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".