Abstract P-132: PEDIATRIC CRITICAL CARE IMPLEMENTATION IN RESOURCE LIMITED SETTINGS: APPLICATION OF COMMUNITY-BASED GROUP MODEL BUILDING
Bibliographic record
Abstract
Aims & Objectives: The introduction of high-quality pediatric critical care medicine (PCCM) in resource-limited settings presents multiple challenges, including the need to coordinate medical care provided by different stakeholders to ensure successful service provision. We aimed to test the use of community-based group model building (GMB) as an intervention to address challenges faced in opening a new Pediatric Critical Care Unit (Mercy James Center for Pediatric Surgery and Critical Care (MJC)) in Blantyre, Malawi. Methods We used GMB to engage multiple stakeholders related to MJC in the process of developing a common vision and language for delivering high-quality PCCM. Two facilitators trained in community-based system dynamics designed and led a two-day GMB workshop in Blantyre in October 2017. Results The participatory workshop exposed the divergent goals and challenges that various stakeholder groups had with respect to the initiation of a new PCCM service at MJC. We developed a causal model (Figure 1) depicting the interrelationships between factors influencing the development of a high-quality PCCM service as a boundary object to negotiate these disparate viewpoints. Group review of the model led to greater consensus and willingness to engage in collaborative problem-solving to achieve common goals. Stakeholders identified strategies to address weaknesses in the causal model for future implementation (Table 1). Figure 1. Causal Model Table 1. Implementation Strategies Conclusions GMB is valuable as a participatory process in addressing various challenges in the implementation and sustainability of high-quality PCCM in resource-limited settings. The next phase involves implementing several impactful strategies to enhance the delivery of PCCM in MJC.
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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 | 0.045 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".