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Record W2986140200 · doi:10.31128/ajgp-03-18-4538

The influence of therapeutic alliance on adult obesity interventions in primary care: A systematic review protocol

2018· review· en· W2986140200 on OpenAlexaff
Elizabeth Sturgiss, Nicholas Elmitt, Jason Agostino, Kirsty Douglas, Alexander M. Clark

Bibliographic record

VenueAustralian Journal of General Practice · 2018
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionMedicineIntervention (counseling)Protocol (science)AllianceSystematic reviewPrimary careManagement of obesityObesityAlternative medicineMEDLINEFamily medicineNursingWeight loss

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Obesity is a common chronic condition, and general practitioners are seeking more effective strategies for assisting their patients. The therapeutic relationship between patients and practitioners is increasingly recognised as a fundamental part of intervention effectiveness. The influence of therapeutic relationships in obesity interventions in primary care has not been systematically studied. We plan to undertake a systematic review and meta-analysis to identify the influence of the therapeutic alliance on the effectiveness of obesity interventions in primary healthcare. The aim of this article is to outline the study protocol. METHOD: A systematic review of primary care interventions for patients with obesity will be undertaken. Using Bordin's framework for the therapeutic alliance, interventions will be categorised as to whether they incorporate the alliance or not. A meta‑analysis will be performed if studies of sufficiently homogenous primary outcome data are found. DISCUSSION: Understanding the role of the therapeutic alliance on interventions for obesity management will have implications for both future intervention development and the translation of current interventions from trial settings to the real world.

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 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)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
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 head0.175
GPT teacher head0.546
Teacher spread0.371 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2018
Admission routes1
Has abstractyes

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