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Record W4284690719 · doi:10.1080/09638288.2022.2094479

Implementation of a biopsychosocial approach into physiotherapists’ practice: a review of systematic reviews to map barriers and facilitators and identify specific behavior change techniques

2022· review· en· W4284690719 on OpenAlexafffund
Jonathan Gervais-Hupé, Arthur Filleul, Kadija Perreault, Anne Hudon

Bibliographic record

VenueDisability and Rehabilitation · 2022
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsBiopsychosocial modelContext (archaeology)Process (computing)Applied psychologyMedicinePsychologyMedical educationPsychotherapistComputer science

Abstract

fetched live from OpenAlex

PURPOSE: Our first objective was to map the barriers and facilitators to the implementation of a biopsychosocial approach into physiotherapists' practice within the Theoretical Domains Framework (TDF). Our second objective was to identify the specific behavior change techniques (BCT) that could facilitate this implementation. MATERIALS AND METHODS: We conducted a review of systematic reviews to identify barriers and facilitators to the use of a biopsychosocial approach by physiotherapists and we mapped them within the TDF domains. We then analyzed these domains using the Theory and Techniques tool (TaTT) to identify the most appropriate BCTs for the implementation of a biopsychosocial approach into physiotherapists' practice. RESULTS: The barriers and facilitators to the use of a biopsychosocial approach by physiotherapists were mapped to 10 domains of the TDF (Knowledge; skills; professional role; beliefs about capabilities; beliefs about consequences; intentions; memory, attention and decision processes; environmental context; social influences; emotion). The inclusion of these domains within the TaTT resulted in the identification of 33 BCTs that could foster the use of this approach by physiotherapists. CONCLUSIONS: Investigating the implementation of a biopsychosocial approach into physiotherapists' practice from a behavior change perspective provides new strategies that can contribute to successfully implement this approach.Implications for RehabilitationThe implementation of a biopsychosocial approach into physiotherapists' practice is a complex process which involves behavior changes influenced by several barriers and facilitators.Barriers and facilitators reported by physiotherapists when implementing a biopsychosocial approach can be mapped within 10 domains of the Theoretical Domain Framework.Thirty-three behavior change techniques (e.g., verbal persuasion about capability, problem solving, restructuring the physical environment, etc.) were identified to foster the implementation of a biopsychosocial approach and specifically target barriers and facilitators.By using a behavior change perspective, this study highlights new strategies and avenues that can support current efforts to successfully implement the use of a biopsychosocial approach into physiotherapists' 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)0.047
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.047
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.199
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0190.018
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.214
GPT teacher head0.580
Teacher spread0.366 · 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 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

Citations32
Published2022
Admission routes2
Has abstractyes

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