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Record W2961273870 · doi:10.1186/s12998-019-0253-z

Guideline implementation in the Canadian chiropractic setting: a pilot cluster randomized controlled trial and parallel study

2019· article· en· W2961273870 on OpenAlexafffundabout
Prakash Dhopte, Simon French, Jeffrey A. Quon, Heather Owens, André Bussières

Bibliographic record

VenueChiropractic & Manual Therapies · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversité du Québec à Trois-RivièresCanadian Chiropractic AssociationCentre Intégré de Santé et de Services Sociaux des LaurentidesMcGill UniversityJewish Rehabilitation HospitalWilliam Osler Health SystemCentre for Interdisciplinary Research in RehabilitationInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersMcGill University
KeywordsGuidelineChiropracticMedicineRandomized controlled trialRehabilitationCluster (spacecraft)Physical therapyMedical physicsAlternative medicineFamily medicineInternal medicinePathologyComputer science

Abstract

fetched live from OpenAlex

Feasibility and pilot studies are recommended prior to embarking on large-scale costly confirmatory trials. The objectives were to determine the feasibility of conducting a cluster randomized controlled trial (C-RCT) to evaluate a complex knowledge translation (KT) intervention to improve the management of people with neck pain, and to identify challenges and potential solutions to conducting a fully powered C-RCT in the chiropractic setting. Pilot C-RCT involving a nationally representative sample of chiropractors and patients. We invited 400 chiropractors and 150 patients to participate. Clinicians were randomized to receive either an online theory-based KT educational and brief action plan (BAP) intervention (intervention group) or a copy of a clinical practice guideline (control group). Study-related challenges were ascertained via mid-study phone interviews and end-of-study feedback questionnaires. Analyses focused on descriptive estimates of likely recruitment, retention, and adherence rates, and documentation of potential barriers. In total, 47 chiropractors (12%) agreed to participate and were randomized after resampling. Fifteen withdrew from the study, leaving a total of 32 (8%) participants. Eleven chiropractors in the intervention group completed the webinars and e-learning modules, two partially completed them and three did not register. Participating chiropractors recruited a total of 29 patients. Sixty-three percent (n = 7) of intervention and 56% (n = 10) of control group patients completed all outcome measures at both baseline and 3-months follow-up, attended follow-up visits and performed home exercises. Patients in the intervention group reported significant reductions in pain (mean 1.6, 95% CI 0.26–2.94, P = 0.027) and disability scores (9.8, 95% CI 3.68–15.91, P = 0.033) from baseline to 3-month follow-up. Key barriers to participation reported by chiropractors included lack of time, difficulties in recruiting patients, problems with the administration of study questionnaires, concern that the clinician-patient relationship might be jeopardized, and lack of assistance from office staff. Over half (55%) of the respondents in the intervention group encountered some difficulty registering or completing the educational modules. Recruitment of clinicians and patients for a trial of a complex intervention can be challenging, and retention of participants after enrolment may be low. Future trials of this nature likely require multiple recruitment strategies to achieve desired sample sizes. Moreover, time-constraint issues are perceived particularly by clinicians as a major barrier to both study enrolment before, and protocol adherence during, their actual participation in a trial. The study was registered at, NCT02483091 , on 17th June 2015.

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)0.245
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2450.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.370
GPT teacher head0.513
Teacher spread0.142 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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

Citations16
Published2019
Admission routes3
Has abstractyes

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