Study on the Knowledge Gap in Training Organized for Traditional Birth Attendants (TBAs) in Rural Guatemala and the Way for Improvement
Bibliographic record
Abstract
Objectives: This study aimed to identify knowledge gaps in the clinical skills of traditional birth attendants TBAs to address them and subsequently help to improve the outcomes of home deliveries in rural Guatemala.An implementation of a basic training program addressed the knowledge gaps.Design and Method: A qualitative study consisting of interviews with 6 practicing traditional birth attendants (TBAs) utilizing qualitative analysis followed by a training session for prenatal care Setting: Q'echi-Mayan speaking communities in a lake.Izabel and hills regions of Guatemala Findings: Significant deficits in knowledge of the TBAs regarding the physiology of pregnancy and skills for management of common high-risk conditions, such as hyperemesis and pregnancy-induced hypertension, that require referral to other skilled birth attendants (SBAs) and to manage these situations early to prevent complications.This study helped to identify barriers faced by traditional birth attendants in the remote Mayan communities.A program that employed a teaching tool based on WHO recommendations were employed to teach TBAs, which was well accepted by TBAs.Results/Conclusions: Traditional birth attendants reported significant knowledge gaps.The study emphasizes the importance of supplementing the training of TBAs with additional periodic training and refreshers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| 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".