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Record W2097546522 · doi:10.1186/s13012-015-0213-5

Fast tracking the design of theory-based KT interventions through a consensus process

2015· article· en· W2097546522 on OpenAlexaffabout
André Bussières, Fadi Al Zoubi, Jeffrey A. Quon, Sara Ahmed, Aliki Thomas, Kent Stuber, Sandy Sajko, Simon French

Bibliographic record

VenueImplementation Science · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsQueen's UniversityUniversité du Québec à Trois-RivièresInternational Collaboration On Repair DiscoveriesUniversity of British ColumbiaCanadian Memorial Chiropractic CollegeMcGill UniversityCentre for Interdisciplinary Research in RehabilitationMcGill University Health Centre
Fundersnot available
KeywordsKnowledge translationPsychological interventionMedicineThematic analysisContext (archaeology)Health carePsychosocialNeck painIntervention (counseling)ChiropracticNursingApplied psychologyMedical educationQualitative researchPsychologyAlternative medicineKnowledge managementPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Despite available evidence for optimal management of spinal pain, poor adherence to guidelines and wide variations in healthcare services persist. One of the objectives of the Canadian Chiropractic Guideline Initiative is to develop and evaluate targeted theory- and evidence-informed interventions to improve the management of non-specific neck pain by chiropractors. In order to systematically develop a knowledge translation (KT) intervention underpinned by the Theoretical Domains Framework (TDF), we explored the factors perceived to influence the use of multimodal care to manage non-specific neck pain, and mapped behaviour change techniques to key theoretical domains. METHODS: Individual telephone interviews exploring beliefs about managing neck pain were conducted with a purposive sample of 13 chiropractors. The interview guide was based upon the TDF. Interviews were digitally recorded, transcribed verbatim and analysed by two independent assessors using thematic content analysis. A 15-member expert panel formally met to design a KT intervention. RESULTS: Nine TDF domains were identified as likely relevant. Key beliefs (and relevant domains of the TDF) included the following: influence of formal training, colleagues and patients on clinicians (Social Influences); availability of educational material (Environmental Context and Resources); and better clinical outcomes reinforcing the use of multimodal care (Reinforcement). Facilitating factors considered important included better communication (Skills); audits of patients' treatment-related outcomes (Behavioural Regulation); awareness and agreement with guidelines (Knowledge); and tailoring of multimodal care (Memory, Attention and Decision Processes). Clinicians conveyed conflicting beliefs about perceived threats to professional autonomy (Social/Professional Role and Identity) and speed of recovery from either applying or ignoring the practice recommendations (Beliefs about Consequences). The expert panel mapped behaviour change techniques to key theoretical domains and identified relevant KT strategies and modes of delivery to increase the use of multimodal care among chiropractors. CONCLUSIONS: A multifaceted KT educational intervention targeting chiropractors' management of neck pain was developed. The KT intervention consisted of an online education webinar series, clinical vignettes and a video underpinned by the Brief Action Planning model. The intervention was designed to reflect key theoretical domains, behaviour change techniques and intervention components. The effectiveness of the proposed intervention remains to be tested.

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)0.230
metaresearch head score (Gemma)0.324
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2300.324
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0070.007
Science and technology studies0.0050.004
Scholarly communication0.0060.006
Open science0.0050.011
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0170.005

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 head0.216
GPT teacher head0.510
Teacher spread0.294 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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".

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Citations33
Published2015
Admission routes2
Has abstractyes

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