Collaborative Groups: Application of a Framework for Interprofessional Collaboration in a High School Setting
Bibliographic record
Abstract
Interprofessional collaboration (IPC), also referred to as interdisciplinary collaboration, is defined in the social work literature as “an effective interpersonal process that facilitates the achievement of goals that cannot be reached when individual professionals act on their own” (Bronstein, 2003, p. 299). IPC is well documented in health care literature and is largely considered best practice in both clinical & educational settings. So much so that the World Health Organization (WHO) developed a Framework for Action on Interprofessional Education (WHO, 2010) and the Canadian Interprofessional Health Collaborative (CIHC) developed a National Interprofessional Competency Framework (CIHC, 2010). According to a systematic review of collaborative models for health and education professionals working in the school settings, models of IPC are described in research but not explicitly evaluated, and there remains a need for robust research in this area (Hillier, Civetta, & Pridham, 2010). This article describes the implementation of an IPC with high school aged students in a special education classroom. The following interconnecting domains from the Canadian National Interprofessional Competency Framework (CIHC, 2010) will be discussed and described: Role Clarification Patient/Client/Family/Community-Centered Team Functioning Collaborative Leadership Interprofessional Communication Interprofessional Conflict Resolution Background considerations, benefits, and barriers will be reviewed also.
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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 | 0.035 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".