Leveraging the strengths of a global network to adapt and sustain interprofessional education and collaborative practice during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic impacted interprofessional education and collaborative practice (IPECP), and global educators collaborated to mitigate the impact. This report reflects the innovations of the global network InterprofessionalResearch.Global (IPR.Global), exploring adaptations and emerging practices in IPECP, and formation of the COVID-19 Taskforce. In response to widespread change and crisis in the pandemic, the Taskforce mobilized global collaboration by forming working groups which led to IPECP innovations through IPR.Global reports, publications, and knowledge forums. Tuckman's theory of group formation is used to explore interprofessional group structures and to understand how network members adapted and collaborated effectively through stages of group development. By leveraging the strengths of IPR.Global, an established global network, adaptations could be made to sustain IPECP in the pandemic, sharing and exploring experiences of emerging best practice through collaborations, group working and knowledge mobilization. Whilst the pandemic impacted IPECP across the world, global networks and teams were key to developing, advancing, and sustaining interprofessional innovations. Through exploring the lessons learned, future collaborations can consider how to promote knowledge mobilization, and sustainability within the global community of practice and advance IPECP by considering team formation theory.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.003 | 0.003 |
| 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".