Problematizing assumptions about interdisciplinary research: implications for health professions education research
Bibliographic record
Abstract
This article critically examines three assumptions underlying recent efforts to advance interdisciplinary research-defined in this article as communication and collaboration between researchers across academic disciplines (e.g. Sociology, Psychology, Biology)-and examines these assumptions' implications for health professions education research (HPER). These assumptions are: (1) disciplines are silos that inhibit the free flowing of knowledge across fields and stifle innovative thinking; (2) interdisciplinary research generates a better understanding of the world as it brings together researchers from various fields of expertise capable of tackling complex problems; and (3) interdisciplinary research reduces fragmentation across groups of researchers by eliminating boundaries. These assumptions are among the new beliefs shaping the contemporary academic arena; they orient academics' and university administrators' decisions toward expanding interdisciplinary research and training, but without solid empirical evidence. This article argues that the field of HPER has largely adopted the premises of interdisciplinary research but has not yet debated the potential effects of organizing around these premises. The authors hope to inspire members of the HPER community to critically examine the ubiquitous discourse promoting interdisciplinarity, and engage in reflection about the future of the field informed by evidence rather than by unsubstantiated assumptions. For example: Should research centres and graduate programs in HPER encourage the development of interdisciplinary or disciplinary-trained researchers? Should training predominantly focus on methods and methodologies or draw more on disciplinary-based knowledge? What is the best route toward increasing the field's profile within academia and attracting the best students and researchers to engage in HPER? These are questions that merit attention at the current juncture as the future of the HPER field relies on decisions made in the present time.
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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 | 0.448 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.022 | 0.274 |
| Scholarly communication | 0.042 | 0.071 |
| Open science | 0.014 | 0.027 |
| Research integrity | 0.018 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".