Creating User Personas to Represent the Needs of Dementia Caregivers Who Support Medication Management at Home: Persona Development and Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Caregiver-assisted medication management plays a critical role in promoting medication adherence and quality of life for people living with Alzheimer disease or related dementias (ADRD). The current landscape of digital and nondigital interventions to support medication management does not meet caregivers' needs, contexts, and levels of technological proficiency. Intervention development can be facilitated using personas or data-driven archetypes that represent end users' traits relevant to solution design. OBJECTIVE: This study aims to understand the strategies and unmet needs of ADRD caregivers who manage medications and use this understanding to create personas that can inform customized caregiver interventions. METHODS: Participants were self-identified primary caregivers of people with ADRD living with or near the care recipient. Virtual contextual inquiry was completed in three stages: (1) enrollment interview, (2) virtual observation over a 1-week period, and (3) postobservation interview. Codebook thematic analysis of interview transcripts was used to identify dimensions of caregivers' approaches to medication management. A reflexive, team-based affinity diagramming approach was used to identify attributes within these dimensions and group attributes into personas. RESULTS: Participants (N=25) were aged 62.32 (SD 11.86) years on average, and 17 (68%) of them were female. Caregivers varied across 6 dimensions relevant to medication management: strategies for medication acquisition, medication storage and organization, medication administration, monitoring the care recipient for symptoms, communication with care network regarding medication, and acquiring information about medication. Three personas were created to represent the observed strategies, unmet needs, and levels of technology use related to medication management: Checklist Cheryl, in Control; Social Sam, Keeps it Simple; and Responsive Rhonda, Stays Relaxed. CONCLUSIONS: Caregivers in this study demonstrated a range of characteristics and values that informed their approach to medication management. They used a combination of technology-based strategies and strategies situated in their physical environments to manage medications. The personas created can be used to inform interventions, such as digital tools, that address caregivers' unmet needs.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".