Assessing Health Needs of the Women of Kolli, Benin: A Participatory Qualitative Study and Recommendations for Academic Medical Missions
Bibliographic record
Abstract
Background: This study was conducted in the rural village of Kolli, Benin, where Ottawa-based medical trainees participate annually in a medical mission delivering primary care and health promotion activities. Academic international medical electives are often related to students’ training rather than the host community’s health needs. A health needs assessment study is an essential tool to orient medical mission goals toward empowering community members to find solutions to unmet needs. Objective: To evaluate Kolli women’s health needs; to identify solutions to health needs in concert with participants; and to guide future medical missions. Methods: A community-based qualitative participatory study was conducted in November 2011. Data was collected through four focus groups with women-participants and semi-structured interviews with key health informants. Women participants were recruited using a purposive sampling based on mixed age categories. Key health informants were recruited by quota based on their inclusion in sanitary decisions and/or medical contact with women from the community. Results: Women participants (n=55) and key health informants (n=7) identified two main health priorities: 1) lack of family planning and 2) poor access to healthcare. Solutions suggested to address these priorities were mostly in the area of education, including health education sessions aimed at women, men, and health providers. Conclusion: Future mission goals in Kolli, Benin, are to expand its current clinical components to a more community needs-based educational approach. For medical trainees, this represents an excellent training opportunity to develop the key competencies of health advocacy patient-centered communication, and medical expertise.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".