What about us?: a call to include oral health professions within interprofessional education for collaborative practice
Bibliographic record
Abstract
Interprofessional collaboration (IPC) among the health professions is recognised as a vital component of efficient health systems and comprehensive healthcare teams. Interprofessional education for collaborative practice (IPECP) is foundational for health professional students to gain an understanding of professional roles, responsibilities, and the value of other professions to patient care. Oral health professionals are highly skilled and knowledgeable experts who recognise the oral-systemic health link. However, they have been largely excluded from, and underutilised within primary healthcare settings and interprofessional teams. Given that oral health is a key indicator of overall health and wellbeing, there is a need mobilise oral health professionals within primary healthcare practice. Yet, advancements for IPECP in oral health education face significant barriers which impede the integration of the oral health professions within interprofessional teams. Collaborative approaches across health programmes to devise intentional, authentic, and transformative strategies for IPECP are needed to bridge gaps in patient care and to dismantle problematic perceptions of 'oral health' as distinct from overall health and wellbeing in contemporary healthcare practice.
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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 | 0.097 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.035 |
| Scholarly communication | 0.036 | 0.050 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.038 | 0.052 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".