TEN EPIQ STEPS TO IMPROVING CARE AND OUTCOMES IN RURAL TANZANIA
Bibliographic record
Abstract
Abstract BACKGROUND Maternal and child health (MCH) remains a priority globally and in Mwanza Region, Tanzania, where under-five mortality exceeds 100/1000 live births. Facility-based Quality Improvement (QI) is critical to enhancing MCH service delivery. The ‘Evidence-based Practice for Improving Quality (EPIQ)’ approach aims to equip clinical providers with basic QI understanding and skills. In Canada, EPIQ introduction has led to significant newborn intensive care improvements. OBJECTIVES To adapt, pilot and evaluate an EPIQ workshop for rural Tanzania. DESIGN/METHODS The 10-step EPIQ process is introduced through a practical, clinically-focused, hands-on workshop. A team of Canadian EPIQ experts with African partnership experience initially adjusted the EPIQ package for a Tanzanian pilot. They then facilitated a one day EPIQ training-of-trainers to QI-interested Tanzanian university faculty and health managers. The new facilitators then delivered the training to selected local health providers. The EPIQ training was evaluated through facilitator and provider (1) pre/post QI-focused knowledge tests, (2) pre/post QI confidence surveys and (3) post-workshop satisfaction surveys. Post training semi-structured group interviews also collected feedback on workshop content and format from facilitators. Written surveys and tests were analyzed using descriptive statistics and aggregate scores; semi-structured interview notes were reviewed for key themes, recommendations and representative quotations. RESULTS Workshop content including flipcharts, workbooks and locally-relevant cases were prepared. In September 2017, workshops were delivered to 16 facilitators and 29 providers in Mwanza. Post-workshop, overall knowledge scores increased by +7.6% (facilitators) and +2.1% (providers). Aggregate confidence survey scores (5-point Likert) increased for both groups by a median increase of 1 (20%). Participant workshop satisfaction scores averaged 4.7/5. Interviews suggested indicated content was perceived as very relevant however too heavy for allocated time; certain wording was too complex for the language setting. Participants reported that the small group and hands-on format encouraged skill development and EPIQ tools equipped for critical thinking and problem solving. It was strongly recommended to increase workshop length to two days. CONCLUSION A clinically-focused, participatory EPIQ training was feasible and well-received in this setting. Despite self-reported confidence increase, a lacking associated knowledge increase may reflect content too complex, limited workshop duration or poor knowledge test face validity. Future considerations should include revised content and/or knowledge testing tool, extended length, and post-workshop implementation follow-up.
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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 | 0.056 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".