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Record W4392357805 · doi:10.2196/49492

Using mHealth to Improve Communication in Adult Day Services Around the Needs of People With Dementia: Mixed Methods Assessment of Acceptability and Feasibility

2024· article· en· W4392357805 on OpenAlexvenueno aff
Amy Zheng, Marissa Bergh, Komal Patel Murali, Tina Sadarangani

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of Health
KeywordsmHealthWorkflowNursingPsychologyHealth careQualitative propertyMedicineMedical educationKnowledge managementApplied psychologyPsychological interventionComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Adult day services (ADS) provide community-based health care for older adults with complex chronic conditions but rely on outdated methods for communicating users' health information with providers. CareMOBI, a novel mobile health (mHealth) app, was developed to address the need for a technological platform to improve bidirectional information exchange and communication between the ADS setting and providers. OBJECTIVE: This study aims to examine the feasibility and acceptability of CareMOBI in the ADS setting. METHODS: A concurrent-triangulation mixed methods design was used, and participants were client-facing ADS staff members, including direct care workers (paid caregivers), nurses, and social workers. Interviews were conducted to describe barriers and facilitators to the adoption of the CareMOBI app. The acceptability of the app was measured using an adapted version of the Technology Acceptance Model questionnaire. Data were integrated into 4 themes as anchors of an informational matrix: ease of use, clinical value, fit within workflow, and likelihood of adoption. RESULTS: A mix of ADS staff (N=22) participated in the study. Participants reported high levels of acceptability across the 4 domains. Qualitative findings corroborated the questionnaire results; participants viewed the app as useful and were likely to implement CareMOBI in their practice. However, participants expressed a need for proper training and technical support throughout the implementation process. CONCLUSIONS: The CareMOBI app has the potential to improve care management in the ADS setting by promoting effective communication through an easy-to-use and portable method. While the integration of CareMOBI is acceptable and feasible, developing role-specific training modules and technical assistance programs is imperative for successful implementation within the ADS setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.085
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.449

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.146
GPT teacher head0.604
Teacher spread0.458 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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