Contextualizing mobility during the Ebola epidemic in Liberia
Bibliographic record
Abstract
Based on findings from focus groups and key informant interviews conducted at five sites in Liberia between 2018 and 2019, we explore some of the key factors that influenced people's motivation to travel during the 2014-2016 Ebola Virus Disease (EVD). We discuss how these factors led to certain mobility patterns and the implications these had for EVD response. The reasons for individual mobility during the epidemic were multiple and diverse. Some movements were related to relocation efforts as people attempted to extricate themselves from stigmatizing situations. Others were motivated by fear, convinced that other communities would be safer, particularly if extended family members resided there. Individuals also felt compelled to travel during the epidemic to meet other needs and obligations, such as attending burial rites. Some expressed concerns about obtaining food and earning a livelihood. Notably, these latter concerns served as an impetus to travel surreptitiously to evade quarantine directives aimed specifically at restricting mobility. Improvements in future infectious disease response could be made by incorporating contextually-based mobility factors, for example: the personalization of public health messaging through the recruitment of family members and trusted local leaders, to convey information that would help allay fear and combat stigmatization; activating existing traditional community surveillance systems in which entry into the community must first be approved by the community chief; and increased involvement of local leaders and community members in the provision of food and care to those quarantined so that the need to travel for these reasons is removed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".