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Record W3176110920 · doi:10.1352/1944-7558-126.4.289

A Randomized Controlled Trial Evaluating a Pain Training for Respite Workers Supporting Children With Disabilities Part Two: Training Evaluations and the Impact of Training on Knowledge Application

2021· article· en· W3176110920 on OpenAlexafffund
Lara M. Genik, Elisabeth L. Aerts, Hiba Nauman, Chantel C. Barney, Stephen P. Lewis, C. Meghan McMurtry

Bibliographic record

VenueAmerican Journal on Intellectual and Developmental Disabilities · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsRespite careRandomizationRandomized controlled trialTraining (meteorology)Physical therapyPain assessmentMedicineFocus groupPsychologyPain management

Abstract

fetched live from OpenAlex

Within a parallel-group randomized control trial, pain training's impact on Respite Workers' (RW) care approaches and training evaluations was explored. RW (n = 158) from 14 organizations received pain or control training following randomization. Researchers were blind until randomization; allocations were not shared explicitly with organizations/participants. Participants completed a strategy use questionnaire immediately before and an evaluation immediately after training completion. Four-to-6 weeks later, participants completed the strategy use questionnaire and semistructured focus groups. No differences in pain approaches were noted in strategy use questionnaires. Per focus groups, both groups acquired a "knowing" about pain and applied pain-related care approaches in similarly. Pain training participants identified need for "growing and strengthening" pain knowledge. Training endorsements were favorable. RW pain training has value and may impact practice.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.054
GPT teacher head0.384
Teacher spread0.330 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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Same venueAmerican Journal on Intellectual and Developmental DisabilitiesSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207