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Record W2727910633 · doi:10.1093/geroni/igx004.2209

A RANDOM CLINICAL TRIAL OF THE EFFECTIVENESS OF DIGITAL AND PAPER CAREGIVING TOOLS

2017· article· en· W2727910633 on OpenAlexaff
Raza Mirza, Patricia A. Donahue

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsThe King's UniversityWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsNull hypothesisIntervention (counseling)Clinical trialRandomized controlled trialPsychologySample (material)Sample size determinationRandom assignmentMedical educationClinical psychologyMedicineGerontologyPsychiatryStatisticsMathematics

Abstract

fetched live from OpenAlex

This research is part of a multi-method research program that evaluated the impact of knowledge mobilization of evidence-based information for older adults, professionals and caregivers. This research reports the results from nine caregiving clinical trials with one group of older adults, one professional group and one of carers, exposed to three levels of the intervention in each trial (n=261). The null hypotheses were that caregiving knowledge would not differ across the three conditions of no tools, paper tools or digital tools. Respondents were randomly assigned to one of three conditions for each of the three groups. The computed sample size required 29 respondents assigned to each of the three conditions for each of the three groups Respondents were tested on knowledge of caregiving pre-post. F tests showed there were significant differences across each of the three conditions. Professionals achieved higher scores with digital tools and older adults, the paper tools.

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)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.098
GPT teacher head0.393
Teacher spread0.295 · 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 designRandomized trial
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

Citations0
Published2017
Admission routes1
Has abstractyes

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