The development of a global chiropractic rehabilitation competency framework by the World Federation of Chiropractic
Bibliographic record
Abstract
The World Health Organization (WHO), in its "Rehabilitation 2030 A Call for Action", identified the need to strengthen rehabilitation in health systems to meet the growing demands of current and future populations. Greater access to rehabilitation services is required to secure the achievement of the United Nation's third Sustainable Development Goal, "Ensure healthy lives and promote well-being for all at all ages". To support this mandate, WHO issued a call for non-governmental organizations, associations and institutions to share their rehabilitation-related competency frameworks which will be used to construct a global rehabilitation competency framework. In response to this call, the World Federation of Chiropractic (WFC) developed a chiropractic rehabilitation competency framework. In this article, we present the chiropractic rehabilitation competency framework that will contribute to the development of the global framework in support of WHO's strategic planning for rehabilitation. The goal of WHO's strategic planning is to improve the integration and support of multi-disciplinary rehabilitation and establishing opportunities for global networks and partnerships in rehabilitation.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".