Getting underneath IPEC competencies: Core Competency 4: Teams and teamwork
Bibliographic record
Abstract
The Interprofessional Education Collaborative (IPEC) formed in 2009 provided significant guidance to advance interprofessional collaboration in its publication of the IPEC competencies in 2011, which described Four Domains and associated competencies to address interprofessional education and practice. Its updated publication in 2016 included public health and the care of populations and clarified its intent that interprofessional collaboration is the overarching theme of the now renamed 4 Core Domains to 4 Core Competencies. The article examines the literature that correlates with the sub competency statements represented within Core Competency 4: Teams and Teamwork (TT) to identify the underpinnings that support their fulfillment. The TT core statement is broad: “Apply relationship-building values and the principles of team dynamics to perform effectively in different team roles…”. There is also considerable overlap between the sub-competency statements. Though the existing literature describes structural characteristics and behavioral elements of good functioning teams, the repertoire is not collectively accessible and assimilated into a whole, but is fragmented, embedded in multiple sources. The article integrates and assembles the qualities of teams and team-members likely to be successful while getting underneath the competency statements to identify the mechanisms and dispositions that drive those competencies. The exploration begins with the structural components of teams and then proceeds to key attributes of teams and team members. The article provides a nexus to correlate IPEC’s TT’s sub-competencies to yield favorable team functioning from which academic institutions, and health care professionals might enrich their knowledge about what works.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".