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Record W2953786777 · doi:10.1159/000497805

EVALUATE-AD and Tele-STAR: Novel Methodologies for Assessment of Caregiver Burden in a Telehealth Caregiver Intervention – A Case Study

2019· article· en· W2953786777 on OpenAlexaff
Neil Thomas, Allison Lindauer, Jeffrey Kaye

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsBruyèreUniversity of Ottawa
FundersNational Institute on AgingNational Institutes of Health
KeywordsTelehealthCaregiver burdenIntervention (counseling)TelemedicinePsychologyDepression (economics)Assisted livingDementiaMedicineClinical psychologyGerontologyPhysical therapyPsychiatryHealth careDisease

Abstract

fetched live from OpenAlex

AIMS: We explored the relationship between objective and subjective measures of burden prior to and after a telehealth-based caregiver intervention. One caregiver participated in two studies, one to assess the feasibility of objective, home-based monitoring (EVALUATE-AD), the second to assess the feasibility of a caregiver education telehealth-based intervention, Tele-STAR. METHODS: Subjective measures of burden and depression in Tele-STAR and objective measures related to daily activities of the caregiver in EVALUATE-AD were compared to examine trends between the different outcome measures. RESULTS: While the caregiver reported an increase in distressing behaviors by her partner, burden levels did not significantly change during or after the Tele-STAR intervention, while objective measures of activity and sleep showed a slight decline. CONCLUSION: Unobtrusive home-based monitoring may provide a novel, objective method to assess the effectiveness of caregiver intervention programs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.404
Teacher spread0.354 · 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 teacher head, not a consensus.

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

Citations20
Published2019
Admission routes1
Has abstractyes

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