Conducting operational research in humanitarian settings: is there a shared path for humanitarians, national public health authorities and academics?
Bibliographic record
Abstract
In humanitarian contexts, it is a difficult and multi-faceted task to enlist academics, humanitarian actors and health authorities in a collaborative research effort. The lack of research in such settings has been widely described in the past decade, but few have analysed the challenges in building strong and balanced research partnerships. The major issues include considering operational priorities, ethical imperatives and power differentials. This paper analyses in two steps a collaborative empirical endeavour to assess health service utilization by Syrian refugee and Lebanese women undertaken by the International Committee of the Red Cross (ICRC), the Lebanese Ministry of Public Health (MoPH) and the Harvard François-Xavier Bagnoud (FXB) Center. First, based on challenges documented in the literature, we shed light on how we negotiated appropriate research questions, methodologies, bias analyses, resource availability, population specificities, security, logistics, funding, ethical issues and organizational cultures throughout the partnership. Second, we describe how the negotiations required each partner to go outside their comfort zones. For the academics, the drivers to engage included the intellectual value of the collaboration, the readiness of the operational partners to conduct an empirical investigation and the possibility that such work might lead to a better understanding in public health terms of how the response met population needs. For actors responding to the humanitarian crisis (the ICRC and the MOPH), participating in a technical collaboration permitted methodological issues to be worked through in the context of deliberations within the wider epistemic community. We find that when they collaborate, academics, humanitarian actors and health authorities deploy their respective complementarities to build a more comprehensive approach. Barriers such as the lack of uptake of research results or weak links to the existing literature were overcome by giving space to define research questions and develop a longer-term collaboration involving individual and institutional learning. There is the need ahead of time to create balanced decision-making mechanisms, allow for relative financial autonomy, and define organizational responsibilities. Ultimately, mutual respect, trust and the recognition of each other's expertise formed the basis of an initiative that served to better understand populations affected by conflict and meet their needs.
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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 | 0.706 | 0.654 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.026 | 0.140 |
| Scholarly communication | 0.069 | 0.078 |
| Open science | 0.012 | 0.046 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".