A framework for interprofessional team collaboration in a hospital setting: Advancing team competencies and behaviours
Bibliographic record
Abstract
Healthcare teams that practice collaboratively enhance the delivery of person-centred care and improve patient and systems outcomes. Many organizations have adopted existing interprofessional frameworks that define the competencies of individual health professionals that are required to meet practice standards and advance interprofessional goals. However, to support the collective efforts of team members to deliver optimal care within complex hospital settings, healthcare organizations may benefit from adopting team-based competencies for interprofessional collaboration. The Sunnybrook framework for interprofessional team collaboration was intentionally created as a set of collective team competencies. The framework was developed using a comprehensive literature search and consensus building by a multi-stakeholder working group and supported by a broad consultation process that included patient representation, organizational development and leadership, and human resources. The six core competencies are actionable and include associated team behaviours that can be easily referenced by teams and widely implemented across the hospital.
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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 | 0.031 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".