Supporting Individuals With Cognitive Impairment and Family Members in Rural Communities: Protocol for a Mixed Methods Digital Health Study
Bibliographic record
Abstract
Background: The health and economic burdens of Alzheimer disease and related dementias (ADRD) are exacerbated for people living in rural social contexts who experience geographic barriers to care. There are currently few resources specifically designed to support socioculturally diverse rural ADRD care dyads, including early detection of potential precursors to ADRD such as mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Objective: The primary objective of the Northern Arizona Memory Study (NAZMS) is to develop culturally informed and scalable resources to identify and support rural families at risk for ADRD. The purpose of this study is to introduce the NAZMS protocol and discuss its role in addressing dementia risk and promoting cognitive health in rural communities. Methods: This dyadic study will use a mixed methods, digital health approach. A sample of rural care dyads with MCI or SCD will be screened and recruited through partnerships with community centers across Northern Arizona. Consenting dyads will complete semistructured interviews to answer questions about technology preferences for monitoring symptoms and engaging in remotely delivered interventions. Next, care dyads will complete separate baseline questionnaires assessing dyadic (eg, experiences with caregiving or care receiving) and health factors. Participants with cognitive impairment will then complete a 14-day mobile protocol of brief end-of-day surveys and cognitive assessments delivered via study-provided smartphones. Results: Data from the qualitative interviews will provide dyad preferences for intervention development. Data from the quantitative protocol will specify for whom (ie, between-person) and on which days (ie, within-person) modifiable factors are related to better cognitive health in everyday life. Conclusions: This study will take a mixed methods, digital health approach to supporting rural families at risk for ADRD by understanding intervention preferences and identifying the modifiable protective and risk factors that influence cognitive health in everyday life. The findings are expected to directly support rural Arizonans and respond to national priorities in ADRD research for the development of community-based disease education programs and the use of digital assessments of cognitive health and 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 | 0.058 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.088 | 0.015 |
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".