Interdisciplinary Research in Assessing Relationships between Environmental Mixtures and Birth Outcomes: What Are the Essential Components for Collaboration?
Bibliographic record
Abstract
Despite the potential utility of interdisciplinary approaches in addressing complex environmental health research questions, there is paucity of research. The Data mining & Neonatal outcomes (DoMiNO) project is an interdisciplinary research which explored relationships between environmental mixtures and adverse birth outcomes using innovative data mining methods. The project applied a collaborative approach building on participation, expertise and perspectives of researchers, clinicians, and knowledge-users to gain new knowledge and facilitate knowledge translation.To better understand collaborative, interdisciplinary research, we used qualitative case study methodology to identify essential components that may support the interdisciplinary environmental research from the team’s experience. Using the DoMiNO project as an exemplar case study of collaborative research, data were obtained from a focus group with ten DoMiNO team members. Applying thematic analysis we identified essential structures, mechanisms, and attributes that supported the interdisciplinary team including: keeping open channels for feedback and ongoing rapport; providing different opportunities for engagement, participation and learning; ensuring repetition of backgrounds, methods and processes in an inclusive and supportive environment. These components, built relationships, enabled learning and created bridges between team members and disciplines. Specific motivators for collaboration were identified: individual roles; sharing results; the need to be flexible, patient, open minded/‘let go’ of usual ways of thinking and leadership commitment to the team building process.Interdisciplinary research is a long and complex journey and could be challenged by its nature and context. The components described support the collaborative research process, resulting in a worthwhile experience which provided exciting results and knowledge translation and exchange activities with researchers and knowledge users.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".