Chiropractic international research collaborative (CIRCuit): the development of a new practice-based research network, including the demographics, practice, and clinical management characteristics of clinician participants
Bibliographic record
Abstract
OBJECTIVES: To describe the structure and development of a new international, chiropractic, practice-based research network (PBRN), the Chiropractic International Research Collaborative (CIRCuit), as well as the demographic, practice, and clinical management characteristics of its clinician participants. An electronic survey was used to collect information on their demographics, practice, and clinical management characteristics from clinicians from 17 October through 28 November 2022. Descriptive statistics were used to report the results. BACKGROUND: PBRNs are an increasingly popular way of facilitating clinic-based studies. They provide the opportunity to collaboratively develop research projects involving researchers, clinicians, patients and support groups. We are unaware of any international PBRNs, or any that have a steering group comprised of equal numbers of clinicians representing the different international regions. RESULTS: 77 chiropractors responded to the survey (0.7% of EBCN-FB members). 48 were men (62%), 29 women (38%). Thirty-six (47%) were in North America, 18 (23%) in Europe, and 15 (19%) in Oceania. Participants reported predominantly treating musculoskeletal issues, often with high-velocity, low-amplitude spinal manipulation (95%), but also with soft tissue therapy (95%), exercise (95%), and other home care (up to 100%). METHODS: The development of CIRCuit is described narratively. Members of the Evidence-Based Chiropractic Network Facebook group (EBCN-FB) were invited to become clinician participants by participating in the survey. CONCLUSIONS: This paper describes the development of a new PBRN for chiropractors. It offers a unique opportunity to facilitate the engagement of clinical chiropractors with research, as well as for academics to readily be able to access an international cohort of clinicians to collaboratively develop and conduct research. Although the results of the survey are not statistically generalisable, the initial cohort of CIRCuit clinician participants use similar techniques on similar types of conditions as the profession at large. The international structure is unique among PBRNs and offers the opportunity to help develop innovative research projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".