Interprofessional education and collaborative practice research during the COVID-19 pandemic: Considerations to advance the field
Bibliographic record
Abstract
In the past few months, we have heard repeatedly, “these are unprecedented times”. Truer words may have never been spoken for we find ourselves amid a global pandemic that has created exceptional, unparalleled, and unusual circumstances, affecting learners, faculty/educators, administrators, researchers, practitioners, and service users (patients/clients, families, and communities). The interprofessional education and collaborative practice (IPECP) research community has been affected in a multitude of ways; ways that have encouraged us to become more collaborative and ways that have sometimes set us apart from one another. The changes that we have experienced may leave us wondering whether we are alone in a field that espouses unity and if there is guidance available. In late 2019, InterprofessionalResearch.Global (IPR.Global) and Interprofessional.Global authored a discussion paper to rouse dialogue and offer perspectives for the global IPECP research agenda (Khalili et al., Citation2019). The long-term aim was to advance IPECP theory and research by 2022, through recommendations for research priorities and counsel on theoretical frameworks, research methodologies, and formation of research teams. And then the COVID-19 pandemic hit. All systems were disrupted globally, necessitating rapid transformation to online IPECP and subsequent evaluation of the impact on students, programs, service users, and healthcare systems (Langlois et al., Citation2020). Understandably, many are now asking how to continue to move forward, or even restart, IPECP research in this “new normal”. In response, IPR.Global formed a COVID-19 taskforce, from which this editorial is developed, to shed light on IPR.Global’s proposed recommendations for research teams (Khalili et al., Citation2019) and offer ways to forge ahead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".