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Record W2208468041 · doi:10.1007/s11414-015-9485-4

Treatment Fidelity of a Nurse-Led Motivational Interviewing-Based Pre-Treatment in Pain Rehabilitation

2015· article· en· W2208468041 on OpenAlexfundno aff
Vera‐Christina Mertens, Lars Forsberg, Jeanine Verbunt, Rob Smeets, Mariëlle E. J. B. Goossens

Bibliographic record

VenueThe Journal of Behavioral Health Services & Research · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersDutch Arthritis AssociationReumaNederlandNewfoundland and LabradorKarolinska InstitutetUniversiteit Maastricht
KeywordsMotivational interviewingFidelityHealth psychologyIntervention (counseling)Quality of Life ResearchRehabilitationClinical psychologyMedicinePsychologyInterviewPhysical therapyNursingPublic health

Abstract

fetched live from OpenAlex

Treatment fidelity and proficiency of a nurse-led motivational interviewing (MI)-based pre-treatment and control condition was evaluated. A random sample was scored by means of the Motivational Interviewing Treatment Integrity (MITI) scale, and a second rater was in charge. MI fidelity was satisfactory for three out of five ratings. Most mean ratings were higher in the MI-based intervention, but differences were not statistically significant. The threshold for beginning MI proficiency was only exceeded for one score and one additional measure. In general, higher levels of fidelity in the intervention condition confirmed that MI was partially applied there. Although the quality of MI delivery as well as mixed inter-rater reliabilities of the fidelity scores leaves room for improvement, robust findings between the two raters were found. These results suggest the need for rigor selection of MI counselors on beforehand, and continuous supervision. Furthermore, fidelity check in studies using MI is needed.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.078
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0780.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.431
GPT teacher head0.549
Teacher spread0.119 · 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

Labeled directly by 3 models reading the full record.

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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

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