Bibliographic record
Abstract
Scientific research requires a fair share of common sense and industry in collecting and explaining data. Above all, it requires a community of people who explore similar questions, share their knowledge, and develop common tools and language. I invite you to read the lists of authors in this issue of JRIPE as part of the larger community of research in interprofessional practice and education (IPE). What forges the connection between these authors is not time or space, but the common field of IPE inquiry: the need to understand change in collaborative practice, the context of change, and how effective IPE can be in changing student attitudes, learning, or behaviour. Just et al. offer the first entry with a randomized controlled trial assessing behaviour change among medical and nursing students [1]. Their study—a controlled experiment in a simulated setting—goes beyond self-reported perceptions of change. Instead, they develop and examine specific hypotheses on expected behavioural performance with a clear link to the definition of IPE and its core competencies. The result is a rigorous study that shows that generating empirical evidence on the behavioural effects of IPE is not only possible, but that it can also be elegantly inspired. With Grymonpre et al., we move to a quasi-experimental research design [2]. Using Mezirow’s transformative learning theory [3], the study underlines the value of mixed methods, the complementarity of qualitative and quantitative data, and the importance of adequate sample size. Two pre-experimental studies complete this series of quantitative analyses. Shrader et al., using a pre-post test design, report on a model of interprofessional student service-learning and how it may improve or sustain positive attitudes toward teamwork and professional roles [4]. Next, Guitard et al., using a singlegroup post-test-only design, go beyond students’ attitudes to assess students’ interprofessional learning in a rehabilitation clinical placement. The authors illustrate a commendable effort to assess interprofessional learning and the promise of the Personal Reflective Tool that requires further elaboration and testing [5]. Qualitative analyses in this issue examine change and the context of change. Sterrett points to what may constitute effective ingredients for an interprofessional community of practice among fellowship students [6]. Next, King et al., through a comparative exploration of two different communities of professionals, lead us in a search for clues on what an effective learning workplace can look like [7]. Finally, Gotlib et al., using an ethnographic investigation of a primary-care setting [8], report that change moves from first-order to second-order change: from what proJournal of Research in Interprofessional Practice and Education
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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 | 0.021 | 0.145 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".