Finding a way in and making it stick: an exploration of chiropractor experiences working in team-oriented elite sport practice settings
Bibliographic record
Abstract
Interprofessional healthcare teams have become the benchmark for optimising athlete health and performance in high-stakes sports. Despite a history of utility as provider partners, chiropractors are currently a relatively underutilised human resource in this rapidly developing and challenging field. Consequently, our study explored the global experiences and distinct perspectives of elite-level career sports chiropractors. Through a qualitative explorative single case study, we purposively sampled and interviewed 15 chiropractors active in elite-level athletic contexts. ‘Professional characteristics and competencies’, ‘Running the gamut of professional career development’ and ‘Navigating team development in a small organisational structure’ emerged as the three key themes from the data. Our data indicate that chiropractors gain provider as members of the elite athletic health and performance management team as multirole manual medicine practitioners. However, thriving in a team-oriented practice, this context appears to be reliant on their capacity for development as part of a small organisational group.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".