Enhancing comprehensive primary care by integrating chiropractic led musculoskeletal care into interprofessional teams through supporting education, competency attainment, and optimizing integration
Bibliographic record
Abstract
Musculoskeletal (MSK) conditions are the leading cause of disability, resulting in up to 40% of visits to family physicians. Current primary care workforce shortages in Canada require other providers to maximize scopes of practice. Few MSK providers have been trained in team-based primary care settings. Study objectives included: (1) educating participating primary care teams through synchronous education, (2) educating Canadian primary care providers through asynchronous education, and (3) integrating chiropractors into primary care teams, whilst evaluating team MSK care knowledge/attitudes and integration experience. Results indicated improvements in collaborative competency, improved understanding and attitudes to chiropractic, and the importance of providing MSK care within funded primary care. Teams employed unique approaches to integrating chiropractors and indicated high demand for their services by patients and providers. Provision of MSK care without economic barrier is desirable and highly valued by teams. Chiropractors are well suited to participate in funded primary care teams in Canada.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".