Lessons Learned Preparing Volunteer Midwives for Service in Haiti: After the Earthquake
Bibliographic record
Abstract
INTRODUCTION: Midwives for Haiti is an organization that focuses on the education and training of skilled birth attendants in Haiti, a country with a high rate of maternal and infant mortality and where only 26% of births are attended by skilled health workers. Following the 2010 earthquake, Midwives for Haiti received requests to expand services and numerous professional midwives answered the call to volunteer. This author was one of those volunteers. The purpose of the study was: 1) to develop a description of the program's strengths and its deficits in order to determine if there was a need to improve the preparation of volunteers prior to service and 2) to make recommendations aimed at strengthening the volunteers' contributions to the education of Haiti and auxiliary midwives. METHODS: Three distinct but closely related questionnaires were developed to survey Haitian students, staff midwives, and volunteers who served with Midwives for Haiti. Questions were designed to elicit information about how well the volunteers were prepared for their experience, the effectiveness of translation services, and suggestions for improving the preparation of volunteers and strengthening the education program. RESULTS: Analysis of the surveys of volunteers, staff, midwives, and the Haitian students generated several common themes. The 3 groups agreed that the volunteers made an effective contribution to the program of education and that the volunteer midwives need more preparation prior to serving in Haiti. The 3 groups also agreed on the need for better translators and recommended more structure to the education program. DISCUSSION: The results of this study are significant to international health care organizations that use volunteer health care professionals to provide services. The results support a growing body of knowledge that international health aid organizations may use to strengthen the preparation, support, and effectiveness of volunteer health providers.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".