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Record W4393933683 · doi:10.2196/54168

Development and Evaluation of a Clinician-Vetted Dementia Caregiver Resources Website: Mixed Methods Approach

2024· article· en· W4393933683 on OpenAlexvenueno aff
Jaye McLaren, Dat Hoang‐Gia, Eugenia Dorisca, Stephanie Hartz, Stuti Dang, Lauren R. Moo

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNIH Clinical CenterOffice of Rural HealthGeriatric Research Education and Clinical CenterU.S. Department of Veterans Affairs
KeywordsDementiaResource (disambiguation)MedicineUsabilityGeriatricsNursingMedical educationGerontologyPsychologyDiseasePsychiatryComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: About 11 million Americans are caregivers for the 6.7 million Americans currently living with dementia. They provide over 18 billion hours of unpaid care per year, yet most have no formal dementia education or support. It is extremely difficult for clinicians to keep up with the demand for caregiver education, especially as dementia is neurodegenerative in nature, requiring different information at different stages of the disease process. In this digital age, caregivers often seek dementia information on the internet, but clinicians lack a single, reliable compendium of expert-approved digital resources to provide to dementia caregivers. OBJECTIVE: Our aim was to create a dementia caregiver resources website to serve as a hub for user-friendly, high-quality, and expert-reviewed dementia educational resources that clinicians can easily supply to family caregivers of people with dementia. METHODS: An interdisciplinary website development team (representing dementia experts from occupational therapy, nursing, social work, geriatrics, and neurology) went through 6 iterative steps of website development to ensure resource selection quality and eligibility rigor. Steps included (1) resource collection, (2) creation of eligibility criteria, (3) resource organization by topic, (4) additional content identification, (5) finalize resource selection, and (6) website testing and launch. Website visits were tracked, and a 20-item survey about website usability and utility was sent to Veterans Affairs tele-geriatrics interdisciplinary specialty care groups. RESULTS: Following website development, the dementia caregiver resource website was launched in February 2022. Over the first 9 months, the site averaged 1100 visits per month. The 3 subcategories with the highest number of visits were "general dementia information," "activities of daily living," and "self-care and support." Most (44/45, 98%) respondents agreed or strongly agreed that the website was easy to navigate, and all respondents agreed or strongly agreed that the resources were useful. CONCLUSIONS: The iterative process of creating the dementia caregiver resources website included continuous identification, categorization, and prioritization of resources, followed by clinician feedback on website usability, accessibility, and suggestions for improvement. The website received thousands of visits and positive clinician reviews in its first 9 months. Results demonstrate that an expert-vetted, nationally, and remotely available resource website allows for easy access to dementia education for clinicians to provide for their patients and caregivers. This process of website development can serve as a model for other clinical subspecialty groups seeking to create a comprehensive educational resource for populations who lack easy access to specialty care.

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)0.107
metaresearch head score (Gemma)0.080
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: none
Teacher disagreement score0.107
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.080
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0050.003
Open science0.0040.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.147
GPT teacher head0.522
Teacher spread0.375 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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