The Ontario Chiropractic Association's Evidence-Based Framework Advisory Council: Enhancing patient care through the comprehensive integration of the pillars of evidence-based practice.
Bibliographic record
Abstract
Supporting chiropractors to deliver Evidence Based Care (EBC) is an important role that professional organizations fulfill in the practice ecosystem. This is a journey that can be accelerated when there is a shared understanding of the elements of Evidence Based Practice (EBP) and the benefits that accrue when applied comprehensively to patient care. The Ontario Chiropractic Association (OCA) undertook a significant project to advance this understanding and enhance these benefits. This paper describes the principles, processes and outputs of our work, which is a series of papers examining the EBP framework in detail. It details why this work is necessary for the chiropractic profession, how it was accomplished, and introduces the themes of each of the six other papers in the series. We aim to support chiropractors in delivering comprehensive care through the application of the evidence-based framework enabling them to practice within the full chiropractic scope of practice in compliance with applicable regulations and legislations. Author’s Note: This paper is one of seven in a series exploring contemporary perspectives on the application of the evidence-based framework in chiropractic care. The Evidence Based Chiropractic Care (EBCC) initiative aims to support chiropractors in their delivery of optimal patient-centred care. We encourage readers to review all papers in the series.
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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 | 0.130 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 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".