Leadership and capacity building in international osteopathic research: Introducing Strengthening osteopathy leadership and research (SOLAR) program
Bibliographic record
Abstract
Research evidence has become the foundation of modern health services. Health professionals rely on sound research to provide safe and effective care for patients, for the development of innovative diagnostic and treatment practices and to develop policies supporting the provision of optimal healthcare. Osteopathy is an established profession with an emerging research evidence base. The Strengthening Osteopathic Leadership and Research (SOLAR) program is a recent international initiative aiming to further build the evidence base and research capacity of the osteopathy profession. The program was developed by The Australian Research Consortium in Complementary and Integrative Medicine (ARCCIM) at the University of Technology Sydney (Sydney, Australia) and funded primarily by Osteopathy Australia, with the support from the Osteopathic Foundation (UK), Osteopaths New Zealand (NZ), Unité Pour l’Ostéopathie (France), and Svenska Osteopatförbundet (Sweden). This paper describes the origins, objectives and features of the SOLAR program and outlines the importance of the program for future research and practice in the osteopathy profession. From its beginnings in 2022, to date, the SOLAR program has been highly successful, producing a substantial collection of concrete research and presentations, while enhancing the Fellows' capacity and confidence as leaders, both in osteopathy and the broader healthcare environment.
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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 | 0.043 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".