Board 166 - Program Innovations AbstractA Utilization Focused Evaluation of Simulation within the Emergency Triage Assessment and Treatment (ETAT) Program in Malawi (Submission #969)
Notice bibliographique
Résumé
Introduction/Background Malawi has among the highest pediatric mortality rates in the world, exceeding 120 deaths/1000 admissions in some hospitals.1,2 Fifty to eighty two percent of these deaths occur within 48 hours of admission2,3 and many are attributable to deficiencies in the care received by critically ill children, which may in part be due to inadequate health worker training.2 Recent introduction of educational programs, such as Emergency Triage Assessment and Treatment (ETAT) have reduced mortality by 10% at some centers.3 As ETAT incorporates elements of simulation, national interest in developing simulation training capacity has grown. At the request of the Malawi Ministry of Health (MMoH), members of the International Pediatric Simulation Society (IPSS) have evaluated ETAT, to delineate strengths and weaknesses in simulation pedagogy within the program, and identify opportunities and threats to the development of simulation-based education in the country. Methods An eight person multidisciplinary team of simulation experts from IPSS travelled to Malawi in May 2013 to conduct the evaluation. A utilization focused evaluation framework known as the Context, Input, Process, Product (CIPP)4 model was adopted to guide the process. For each CIPP element, multiple data sources were collected, including field notes and interviews with stakeholders completed during site visits to the MMoH, central and district hospitals, rural healthcare centers and both medical and nursing training colleges; direct observations of an ETAT course; and follow up interviews with faculty and participants. Borrowing on the SWOT (strengths, weaknesses, opportunities and threats) matrix,5 data were organized as drivers (strengths and opportunities) or barriers (weaknesses and threats). Our evaluation revealed that although simulation is incorporated as an educational tool within ETAT, it may be significantly underutilized. Evaluation of context identified primary drivers to be buy in from the MMoH for national scale-up of ETAT and support from faculty for revising the curriculum to align with simulation best practices. Barriers included high patient volumes and staff shortages, limiting time for faculty and participants to attend ETAT training. However, this was also identified as an opportunity to incorporate in-situ simulation into ETAT. The evaluation of input identified access to simulation materials (e.g. mannequins, animal models and patients for ‘clinical practice’) to meet educational needs as a driver. Conversely, the increasing number of trainees and limited number of trainers were identified as barriers. Drivers identified during process evaluation included passionate faculty keenly interested in developing their simulation skills, opportunities for interprofessional education and team training (given ETAT is delivered in an interdisciplinary fashion) and dedicated moments for simulation training within the course. Barriers included lack of faculty training in simulation pedagogy, resulting in limited scenario based training, no debriefing and failure to facilitate deliberate practice.6 Finally, product evaluation revealed that participants perceived ETAT training significantly improved their skills. However, severe clinical resource shortages, resulting in a mismatch between what participants are taught and what they can deliver was identified as a significant barrier to subsequent improvement in pediatric outcomes. Results: Conclusion Recent evidence suggests training health workers through educational programs incorporating simulation significantly impacts pediatric mortality, supporting arguments for capacity development of simulation in Malawi. Our evaluation reveals faculty development and enhancement of simulation pedagogy within ETAT are the most pressing needs in this regard. This may be facilitated through a ‘train the trainers’ program focused on best practices in simulation.7 We are currently developing such a program, with anticipated rollout in 2014. Subsequent evaluation of its impact on the delivery and effectiveness of future ETAT courses is planned. Once a highly trained cadre of simulation educators has been established, development of programs beyond ETAT (e.g. in-situ simulation in healthcare facilities) may be explored. However, in such low resource settings, educational content must be appropriately matched to the realities of clinical practice. References 1. You D, New JR, Wardlaw T: Levels & Trends in Child Mortality. New York, NY: United Nations Children’s Fund 2012; 1-32. Available at: http://www.childinfo.org/files/Child_Mortality_Report_2012.pdf. 2. Lufesi N: Assessment of Hospital Based Child Care Services in Malawi: Final Report. Malawi Ministry of Health Acute Respiratory Infections Control Program; 2010:1-42. 3. Robison JA, Ahmad ZP, Nosek CA, Durand C, Namathanga A, Milazi R, Thomas A, Soprano JV, Mwansambo C, Kazembe PN, Torrey SB: Decreased Pediatric Hospital Mortality After an Intervention to Improve Emergency Care in Lilongwe, Malawi. PEDIATRICS 2012; 130(3):e676-82. 4. Stufflebeam D: The CIPP model for program evaluation, Evaluation models: Viewpoints on educational and human services evaluation. Edited by Madaus G, Scriven M, Stufflebeam D. Boston, Kluwer-Nijhoff, 1983, pp 117-41. 5. Gordon J, Hazlett C, Cate Ten O, Mann K, Kilminster S, Prince K, O’Driscoll E, Snell L, Newble D: Strategic planning in medical education: enhancing the learning environment for students in clinical settings. Medical Education 2000; 34(10):841-850. 6. McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB: Does Simulation-Based Medical Education With Deliberate Practice Yield Better Results Than Traditional Clinical Education? A Meta-Analytic Comparative Review of the Evidence. Academic Medicine. 2011;86(6):706-711. 7. Dorman K, Derbew M, Henok F, Desalegn D, Dubrowski A, Satterthwaite L, Pittini R, Tajirian T, Kneebone R, Bello F, Byrne N: A Training Cascade for Interprofessional Surgical and Obstetrical Care in Ethiopia. In: 2012 Abstracts, Canadian Conference on Global Health 2012: 29. Disclosures Royal College of Physicians and Surgeons of Canada Fellowship for Studies in Medical Education
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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 | 0,021 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,005 |
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 ».