Interprofessional education through shadowing experiences in multi-disciplinary clinical settings
Bibliographic record
Abstract
The World Health Organization has recently added Interprofessional Education (IPE) to its global health agenda recognizing it as a necessary component of all health professionals' education. We suggest mandatory interprofessional shadowing experiences as a mechanism to be used by chiropractic institutions to address this agenda. IPE initiatives of other professions (pharmacy and medicine) are described along with chiropractic. This relative comparison of professions local to our jurisdiction in Ontario, Canada is made so that the chiropractic profession may take note that they are behind other health care providers in implementing IPE.Interprofessional shadowing experiences would likely take place in a multi-disciplinary clinical setting. We offer an example of how two separate professions within a Family Health Team (FHT) can work together in such a setting to enhance both student learning and patient care. For adult learners, using interprofessional shadowing experiences with learner-derived and active objectives across diverse health professional groups may help to improve the educational experience. Mandatory interprofessional shadowing experiences for chiropractors during their training can enhance future collaborative practice and provide success in reaching a goal common to each profession - improved patient care.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".