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Record W4290694916 · doi:10.1177/15598276221117089

A Rubric to Assess the Design and Intervention Quality of Randomized Controlled Trials in Health and Wellness Coaching

2022· review· en· W4290694916 on OpenAlexaff
Sebastian Harenberg, Gary A. Sforzo, Joel S. Edman

Bibliographic record

VenueAmerican Journal of Lifestyle Medicine · 2022
Typereview
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsMedicineRubricCoachingRandomized controlled trialIntervention (counseling)Health coachingPhysical therapyQuality (philosophy)Research designMedical educationFamily medicineNursingSurgeryPsychotherapist

Abstract

fetched live from OpenAlex

Objective: To collect health and wellness coaching (HWC) literature related to treatment of obesity and Type 2 Diabetes (T2D) for systematic assessment using a novel rubric. Data Source: Pubmed, CINAHL, and PsychInfo. Study Inclusion and Exclusion: Given 282 articles retrieved, only randomized and controlled trials meeting a HWC criteria-based definition were included; studies with intervention <4 months or <4 sessions were excluded. Data Extraction: Rubric assessment required details of two theoretical frameworks (i.e., study design and HWC intervention design) be extracted from each included paper. Data Synthesis: Data were derived from a 28-item rubric querying items such as sampling characteristics, statistical methods, coach characteristics, HWC strategy, and intervention fidelity. Results: 29 articles were reviewed. Inter-rater rubric scoring yielded high intraclass correlation (r = .85). Rubric assessment of HWC literature resulted in moderate scores (56.7%), with study design scoring higher than intervention design; within intervention design, T2D studies scored higher than obesity. Conclusions: A novel research design rubric is presented and successfully applied to assess HWC research related to treatment of obesity and T2D. Most studies reported beneficial clinical findings; however, rubric results revealed moderate scores for study and intervention design. Implications for future HWC research are discussed.

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
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.493
metaresearch head score (Gemma)0.681
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.507
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4930.681
Meta-epidemiology (narrow)0.0070.004
Meta-epidemiology (broad)0.0180.030
Bibliometrics0.0480.031
Science and technology studies0.0070.008
Scholarly communication0.0100.007
Open science0.0050.011
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0140.004

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 head0.299
GPT teacher head0.548
Teacher spread0.249 · 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 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
DomainMethods
GenreReview · Methods

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
Published2022
Admission routes1
Has abstractyes

Explore more

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