Methodological gaps and opportunities for studying multisectoral collaboration for health in low- and middle-income countries
Bibliographic record
Abstract
The current body of research into multisectoral collaborations (MSCs) for health raises more questions than it answers, both in terms of how to implement MSCs and how to study them. This article reflects on current methodological gaps and opportunities for advancing MSC research, based on a targeted review of existing literature and qualitative input from researchers and practitioners at the 2018 Health Systems Research (HSR) Symposium in Liverpool. Through framework analysis of 205 MSC research papers referenced in a separately published MSC 'overview of reviews' paper, this article identifies six broad MSC question domains ('meta questions') and applies content analysis to estimate the relative frequency with which these meta questions and the research method(s) used to answer them are present in the literature. Results highlight a preponderance of research exploring MSC implementation using case study methods, which, in aggregate, does not seem to adequately meet policymakers' and practitioners' needs for generalizable or transferable insights. The content analysis is complemented by qualitative insights from HSR Symposium participants and the authors' own experience to identify six key methodological gaps in research on MSC for health. For each of these gaps, we propose areas in which we believe there are opportunities for methodological development and innovation to help advance this field of study, including: better understanding the role of power dynamics in shaping MSCs; development of a classification framework (or frameworks) of governance arrangements; exploring divergence of perspective and experience among MSC partners; identifying or generating theoretical frameworks for MSC that work across sectors and disciplines; developing intermediate indicators of collaboration; and increasing transferability of insights to other contexts. Collaboration with researchers outside of the health sector will enhance efforts in each of these areas, as will the establishment and strengthening of pluralistic MSC evidence networks also involving policymakers and practitioners.
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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 | 0.198 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".