A meeting of minds: interdisciplinary research in the health sciences in Canada
Bibliographic record
Abstract
Brought together by the newly formed Canadian Academy of Health Sciences (CAHS), recognized national leaders in the 6 health sciences disciplines consider the environment for conducting interdisciplinary health research (IDHR) in Canada. Based on first-hand knowledge and thoughtful reflection, the authors argue that although much progress has been made in support of IDHR in Canada, the practical experience of researchers does not always bear this out. This article examines government, industry and academia to identify the cultural and structural characteristics that demand, promote or prevent IDHR in each sector. At its heart is the question, How can universities best support and enhance IDHR, not only for the benefit of science, but also to meet the growing needs of industry and government for intellectual capital? Focusing on the predominant health sciences disciplines, the authors define IDHR as a team of researchers, solidly grounded in their respective disciplines, who come together around an important and challenging health issue, the research question for which is determined by a shared understanding in an interactive and iterative process. In addition, they suggest that IDHR is directly linked to translational research, which is the application of basic science to clinical practice and the generation of scientific questions through clinical observation. This analysis of academic, industry and government sectors is not intended to offer rigorous data on the current state of IDHR in Canada. Rather, the goal is to stimulate research-policy dialogue by suggesting a number of immediate measures that can help promote IDHR in Canada. Recommended measures to support IDHR are aimed at better resourcing and recognition (by universities and granting agencies), along with novel approaches to training, such as government-and industry-based studentships. In addition, we recommend that professional organizations reconsider their policies on publication and governance. Although intended to maintain professional scopes of practice, these policies also serve to entrench disciplinary boundaries in research. We conclude by suggesting a number of research questions for a more rigorous assessment of the climate for IDHR in Canada. We call for an inventory and comparative analysis of academic centres, institutes and consortiums in Canada that strive to facilitate IDHR; an examination of the impact of professional organizations on health research, and on IDHR in particular; and a systematic review of research training opportunities that promote IDHR, with a view to identifying and replicating proven models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.071 | 0.035 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".