Technologies of trust in epidemic response: openness, reflexivity and accountability during the 2014–2016 Ebola outbreak in West Africa
Bibliographic record
Abstract
Trust is an essential component of successful cooperative endeavours. The global health response to the 2014-2016 West Africa Ebola outbreak confronted historically tenuous regional relationships of trust. Challenging sociopolitical contexts and initially inappropriate communication strategies impeded trustworthy relationships between communities and responders during the epidemic. Social scientists affiliated with the Ebola 100-Institut Pasteur project interviewed approximately 160 local, national and international responders holding a wide variety of roles during the epidemic. Focusing on responder's experiences of communities' trust during the epidemic, this qualitative study identifies and explores social techniques for effective emergency response. The response required individuals with diverse knowledges and experiences. Responders' included on-the-ground social mobilisers, health workers and clinicians, government officials, ambulance drivers, contact tracers and many more. We find that trust was fostered through open, transparent and reflexive communication that was adaptive and accountable to community-led response efforts and to real-time priorities. We expand on these findings to identify 'technologies of trust' that can be used to promote actively legitimate trustworthy relationships. Responders engaged the social technologies of openness (a willingness and genuine effort to incorporate multiple perspectives), reflexivity (flexibly responsive to context and ongoing dialogue) and accountability (taking responsibility for local contexts and consequences) to facilitate relations of trust. Technologies of trust contribute to the development of a framework of practical techniques to improve the acceptance and effectiveness of future emergency response strategies.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".