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Record W2724525540 · doi:10.5195/ijt.2017.6214

Feasibility of Using Telehealth to Deliver the “Powerful Tools for Caregivers” Program

2017· article· en· W2724525540 on OpenAlexfundno aff
Katrina M. Serwe, Gayle Hersch, Karen Pancheri

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersConcordia University
KeywordsTelehealthAttendanceVideoconferencingUsabilityTelemedicinePopulationFamily caregiversMedicineSoftware deploymentNursingComputer scienceMultimediaHealth careHuman–computer interaction

Abstract

fetched live from OpenAlex

Caregivers report poorer health and wellness than the general population and identify numerous barriers to their attending programs to improve health and wellness. The purpose of this study was to explore the feasibility of employing a telehealth delivery method to enhance access to caregiver wellness programs. This article presents the quantitative results of a mixed methods feasibility study of translating the Powerful Tools for Caregivers (PTC) program to a telehealth delivery format. Four unpaid family caregivers of older adults participated in a telehealth delivered PTC program, a wellness program with established outcomes in the in-person environment. The program was delivered using synchronous videoconferencing methods. High class attendance and a high median total average Telehealth Usability Questionnaire score of 5.7 indicated the telehealth delivery method was feasible. This research suggests that telehealth is a feasible delivery format for a caregiver program traditionally delivered in an in-person format.

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)0.014
metaresearch head score (Gemma)0.026
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 score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.132
GPT teacher head0.487
Teacher spread0.355 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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