Opinions of sports clinical practice chiropractors, with sports specialty training and those without, about chiropractic research priorities in sports health care: a centering resonance analysis.
Bibliographic record
Abstract
INTRODUCTION: A Canadian sports chiropractic research agenda has yet to be defined. The Delphi method can be utilized to achieve this purpose; however, the sample of experts who participate can influence the results. To better inform sample selection for future research agenda development, we set out to determine if differences in opinions about research priorities exist between chiropractors who have their sports specialty designation and those who do not. METHODS: Fifteen sports clinical practice chiropractors who have their sports fellowship designation and fifteen without, were interviewed with a set of standardized questions about sports chiropractic research priorities. A centering resonance analysis and cluster analysis were conducted on the interview responses. RESULTS: The two practitioner groups differed in their opinions about the type of research that they would like to see conducted, the research that would impact their clinical practice the most, and where they believed research was lacking. However, both groups were similar in their opinions about research collaborations. CONCLUSION: Sports clinical practice chiropractors, with their sports specialty designation and those without, differed in their opinions about sports chiropractic research priorities; however, they had similar opinions about research collaborations. These results suggest that it may be important to sample from both practitioner groups in future studies aimed at developing research agendas for chiropractic research in sport.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".