TEN EPIQ STEPS TO IMPROVING CARE AND OUTCOMES IN RURAL TANZANIA
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».