Identifying sports chiropractic global research priorities: an international Delphi study of sports chiropractors
Bibliographic record
Abstract
Objectives: Developing a research agenda is one method to facilitate broad research planning and prioritise research within a discipline. Despite profession-specific agendas, none have specifically addressed the research needs of the specialty of sports chiropractic. This study determined consensus on research priorities to inform a global sports chiropractic research agenda. Methods: A Delphi consensus methodology was used to integrate expert opinions. Clinicians, academics and leaders from the international sports chiropractic specialty were recruited using purposive sampling to participate in (1) a Delphi panel involving three voting rounds to determine consensus on research priorities and (2) a priority importance ranking of the items that reached consensus. Results: We identified and contacted 141 participants, with response rates for rounds 1, 2 and 3, of 44%, 31% and 34%, respectively. From the original 149 research priorities, 66 reached consensus in round 1, 63 in round 2 and 45 items in round 3. Research priorities reaching consensus were collapsed by removing redundancies, and priority ranking identified 20 research priorities, 11 related to collaboration and 6 to research themes. Conclusions: The top-ranked items for research priorities, research themes and collaborations included the effects of interventions on performance, recovery and return to play; clinical research in sport; and collaborations with researchers in chiropractic educational institutions, respectively. Implications: The prioritisation of research items can be evaluated by key stakeholders (including athletes) and implemented to develop the first international research agenda for sports chiropractic.
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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 | 0.114 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".