Interdisciplinary health research: perspectives from a process evaluation research team.
Bibliographic record
Abstract
BACKGROUND: Interdisciplinary health research (IDHR) is increasingly encouraged and is often a specific requirement for research grants provided by health research funding councils worldwide. There is consensus that research expertise and scholarship from a diverse range of disciplines are necessary to examine questions relating to complex health and social concerns for which single disciplinary approaches have been found inadequate. METHODS: This paper reports on the experiences of an interdisciplinary process evaluation research team working in the field of stroke care. RESULTS: Realising the perceived benefits is less than straightforward; setting up and conducting IDHR can present researchers with a range of challenges at a strategic, practical and individual level. We identify how differences in disciplinary perspectives and skills impacted on our research practice. CONCLUSIONS: Whilst initially challenging, our different approaches to the research problem and the methods to address it, expanded conceptual and methodological understanding and proved of benefit for the research team and the study outputs.
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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 | 0.517 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.025 | 0.049 |
| Scholarly communication | 0.044 | 0.025 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.016 | 0.023 |
| 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".