Research Priorities of the Australian Chiropractic Profession: A Cross-Sectional Survey of Academics and Practitioners
Bibliographic record
Abstract
Objective The purpose of this study was to explore the research priorities of Australian practicing chiropractors and academics across a set of research domains to determine the agreement or disagreement based on these domains. Methods We conducted a pilot-tested online survey focusing on the following 5 principal research domains: basic science, conditions (disorders chiropractors may encounter), patient subgroups, clinical interventions, and practice and public health/health services. Responses were sought regarding support for funding research scholarships, practice-based research networks, scientific conferences/symposia, journals, and existing research agendas. Data were collected (February 19 to May 24, 2019) from a sample of chiropractic academics (n 1 = 33) representing 4 Australian programs and practicing chiropractors (n 2 = 340). Collected data were ranked and analyzed to determine agreement across domains and items. Results There was agreement between the 2 groups across the majority (>90%) of domain items. The closest agreement and highest rankings were achieved for the "clinical interventions and practice" and "conditions" domains. Disagreement was observed within specific domain items, such as patient subgroups (infants), and for 1 intervention (chiropractic-specific techniques). Disagreement also occurred outside of the main domains, including research agenda support and funding. Conclusions There was overall agreement between practicing chiropractors and academics across most research area domain items, which should help facilitate consensus-led development of any potential Australian Chiropractic research agenda. Disagreements across specific domain items, such as population subgroups, interventions, and funding require further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".