Building organizational and strategic interprofessional collaboration and partnerships: a case study
Bibliographic record
Abstract
Developing organizational strategic partnerships is important to advance initiatives such as research, training/education, and interprofessional collaboration (IPC) with a global perspective. Commitments to collaborative leadership, intentional partnership, coordination, and progress, thematically represent the series of critical decisions and actions collectively required to achieve strategic alliance success. The purpose of this paper is to describe the evidenced-informed framework and systematic processes involved in building successful strategic organizational and collaborative partnerships for InterprofessionalResearch.Global to expand and enhance opportunities for IPC on mutually beneficial initiatives. The conceptual model for effective collaborative partnerships by Butt et al. (2008) provided a framework for InterprofessionalResearch.Global to develop two strategic organizational partnerships consistent with its mission, vision, and goals to explore interprofessional research and policy gaps through global research partnerships, grow and sustain communities of practice, and mobilize evidence-informed interprofessional education and collaborative practice across multiple and diverse contexts. These organizational partnerships are defined by a Memorandum of Understanding with clear expectations and mechanisms of communication, defined priority areas and timelines for collaborative efforts, mutual understanding of the purposes of each relationship, and timeline and expectations for periodic evaluation.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| 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".