Building the capacity of interprofessional providers to work in primary care teams: Insights from six professions
Bibliographic record
Abstract
Context: Interprofessional primary care teams are crucial for providing comprehensive care to patients with complex health needs. For many professions, interprofessional primary care is a new practice setting and understanding the unique collaborative processes within primary care remains limited. Few training resources are available to support primary care teams. Objective: Enhance the capacity of interprofessional primary care providers to work collaboratively through online education modules. Study Design and Analysis: An online consensus building exercise was held to co-develop learning objectives with four representatives from six professions including Audiology, Dietetics, Occupational Therapy, Physiotherapy, Social Work, and Speech-Language Pathology. Key competencies from the Canadian Interprofessional Health Collaborative, including team functioning, collaborative leadership, communication, role clarification, collaborative relationship building, and conflict resolution, guided the development of the modules and engagement process. The interprofessional collaborative relationship-building model informed interprofessional case studies. Setting: On-line primary care modules. Population Studied: Pre-and post-licensure interprofessional primary care providers. Intervention: New curricula for team-based primary care in the form of online modules focusing on the foundations of primary care, including models of team-based care, access, equity, continuity, comprehensiveness. Outcome Measures: The collaborative process led to the development of educational modules addressing core aspects of primary care and enhancing interprofessional collaboration. Results: Results demonstrated successful collaboration with comprehensive primary care modules that address the unique needs of interprofessional teams.The modules provided a foundation for primary care teams to understand their roles, communicate effectively, and resolve conflicts. Recommendations include building capacity within and across professions for primary care teamwork, articulating collaboration competencies within the primary care context, engaging in collaborative educational activities, establishing primary care competencies, and fostering interprofessional collaboration in practice and education. Conclusions: This work highlights the importance of interprofessional collaboration in primary care and underscores the need for ongoing education and support to ensure effective teamwork.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".