Interdisciplinarity in health services research: dreams and nightmares, maladies and remedies
Bibliographic record
Abstract
Interdisciplinarity has become popular in health services research. Advocates suggest that interdisciplinary approaches may produce more accessible, applicable, exciting and realistic knowledge than traditional disciplinary approaches. To date, there has been surprisingly little analysis of the institutional and intellectual demands of interdisciplinarity as a methodology or practice. This paper (1) identifies some basic intellectual and institutional features of interdisciplinary research, (2) describes typical interdisciplinary 'dreams' and corresponding 'nightmares' that researchers might encounter in practice, (3) highlights maladies of interdisciplinary research careers and suggests practical remedies, and (4) discusses implications for health research policy. Individual researchers can avoid pitfalls of interdisciplinarity through strategies that include selective collaboration, cross-training, sustained relationships, good humour, participation in peer review, declaring the place of one's work, and balancing dissemination of research between peer and other audiences. Interdisciplinary activities span institutional boundaries and make novel demands on academic resources and allegiances. Research organizations can improve their hospitality to interdisciplinary work by encouraging straightforward communication, recognising interdisciplinary productivity, making allowances for the higher time and energy costs of interdisciplinary liaisons, and providing the necessary institutional support and stability to cultivate projects to fruition. Alongside the creation of large new interdisciplinary networks and organizations, we should invest in the highly valuable contributions of small and enduring interdisciplinary teams, modest interdisciplinary stretches and evolving interdisciplinary creatures.
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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 | 0.214 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.153 |
| Scholarly communication | 0.027 | 0.043 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.002 | 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".