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Record W3160661965 · doi:10.1016/j.apmr.2021.04.009

The Time Is Now: A FASTER Approach to Generate Research Evidence for Technology-Based Interventions in the Field of Disability and Rehabilitation

2021· article· en· W3160661965 on OpenAlexaff
Rosalie H. Wang, Lisa K. Kenyon, Katherine S. McGilton, William C. Miller, Nina Hovanec, Jennifer Boger, Pooja Viswanathan, Julie M. Robillard, Stephen Czarnuch

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of NewfoundlandGF Strong Rehabilitation CentreUniversity of British ColumbiaUniversity of TorontoUniversity Health NetworkUniversity of WaterlooToronto Rehabilitation Institute
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Evidence-based practiceRehabilitationManagement scienceKnowledge managementImplementation researchProcess managementPsychologyMedicineComputer scienceNursingEngineeringPhysical therapyAlternative medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.488
metaresearch head score (Gemma)0.644
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.512
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4880.644
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0220.008
Science and technology studies0.0050.008
Scholarly communication0.0300.068
Open science0.0080.026
Research integrity0.0210.029
Insufficient payload (model declined to judge)0.0670.008

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 head0.409
GPT teacher head0.657
Teacher spread0.248 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations67
Published2021
Admission routes1
Has abstractno

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