“Confidence comes with frequent practice”: health professionals’ perceptions of using manual vacuum aspiration after a training program
Bibliographic record
Abstract
BACKGROUND: Malawi has one of the highest maternal mortality rates in the world, with unsafe abortion as a major contributor. Curettage is most frequently used as the surgical method for treating incomplete abortions, even though it is costly for an impoverished health system and the less expensive and safe manual vacuum aspiration (MVA) method is recommended. METHODS: The aim of this 2016-17 study is to explore health worker's perception of doing MVA 1 year after an educational intervention. Focus group discussions were recorded, transcribed verbatim, and analyzed using content analysis for interpreting the findings. A knowledge, attitude and practice survey was administered to health professionals to obtain background information before the MVA training program was introduced. RESULTS: Prior to the training sessions, the participants demonstrated knowledge on abortion practices and had positive attitudes about participating in the service, but preferred curettage over MVA. The training was well received, and participants felt more confident in doing MVA after the intervention. However, focus group discussions revealed obstacles to perform MVA such as broken equipment and lack of support. Additionally, the training could have been more comprehensive. Still, the participants appreciated task-sharing and team work. CONCLUSION: Training sessions are considered useful in increasing the use of MVA. This study provides important insight on how to proceed in improving post-abortion care in a country where complications of unsafe abortion are common and the health system is low on resources.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".