Building Interdisciplinary Research Capacity: a Key Challenge for Ecological Approaches in Public Health
Bibliographic record
Abstract
The shortcomings of public health research informed by reductionist and fragmented biomedical approaches and the emergence of wicked problems are fueling a renewed interest in ecological approaches in public health. Despite the central role of interdisciplinarity in the context of ecological approaches in public health research, inadequate attention has been given to the specific challenge of doing interdisciplinary research in practice. As a result, important knowledge gaps exist with regards to the practice of interdisciplinary research. We argue that explicit attention towards the challenge of doing interdisciplinary research is critical in order to effectively apply ecological approaches to public health issues. This paper draws on our experiences developing and conducting an interdisciplinary research project exploring the links among climate change, water, and health to highlight five specific insights which we see as relevant to building capacity for interdisciplinary research specifically, and which have particular relevance to addressing the integrative challenges demanded by ecological approaches to address public health issues. These lessons include: (i) the need for frameworks that facilitate integration; (ii) emphasize learning-by-doing; (iii) the benefits of examining issues at multiple scales; (iv) make the implicit, explicit; and (v) the need for reflective practice. By synthesizing and sharing experiences gained by engaging in interdisciplinary inquiries using an ecological approach, this paper responds to a growing need to build interdisciplinary research capacity as a means for advancing the ecological public health agenda more broadly.
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 | 0.282 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.024 | 0.171 |
| Scholarly communication | 0.042 | 0.049 |
| Open science | 0.009 | 0.083 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".