What motivates individuals to volunteer in Ebola epidemic response? A structural approach in Guinea
Bibliographic record
Abstract
BACKGROUND: The 2014-2016 Ebola epidemic in West Africa placed greater demands on the affected countries' already scarce health workforce. Consequently, governments in the most affected West African countries made appeals for volunteers to join Ebola response programs. Those volunteers played an important yet high-risk role in aiding the victims of the Ebola epidemic and in limiting its spread. However, little is known as to what motivated those volunteers to commit themselves to the Ebola response programs. This information is important for planning for volunteer recruitment strategies during future epidemics. The aim of the present study, therefore, was to identify and assess the motivations that led individuals to volunteer for Ebola response programs in West Africa. METHODS: The study participants were 600 persons who volunteered through the Guinean Ebola response program during the 2014-2016 epidemic. From February to May 2016, they were presented with a questionnaire that contained 50 assertions referring to possible motives for volunteering in the Ebola response program and indicated their degree of agreement with each of them on a scale of 0-10. The responses were analyzed using factor analysis. RESULTS: Seven separable volunteer motivations were identified. "Feeling of patriotic duty" (M = 9.02) and "Feeling of moral responsibility" (M = 8.12) clearly emerged as the most important. Second-tier motivations were "Compliance with authority" (M = 6.66), "Desire to use one's skills for a collective good" (M = 6.49), "Seeking personal growth" (M = 5.93), "Desire to gain community recognition" (M = 5.13), and "Hoping for a career reorientation" (M = 4.52). CONCLUSIONS: These findings strongly suggest that volunteer recruitment, if needed in future Ebola epidemics, must adopt a multifaceted motivational approach rather than focus on one single motivator. Putting relatively more emphasis on motivational messages referring to patriotic values, as well as to moral responsibility, would likely increase volunteering.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".