A view from the top: A qualitative exploration of top‐level health care leaders’ perceptions on the implementation of simulation‐based education in postgraduate medical education
Bibliographic record
Abstract
INTRODUCTION: Although there is substantial evidence supporting the benefits of simulation-based education (SBE), its widespread and effective implementation remains challenging. The aim of this study was to explore the perceptions of top-level health care leaders regarding SBE and the barriers and facilitators that influence its wide implementation as part of the postgraduate surgical curricula in Denmark. METHODS: We conducted semi-structured interviews with top-level health care leaders who were chosen based on their roles in ensuring high-quality patient care delivery and developing strategies to achieve the goals of the entire health care system. The interview transcripts were translated into English, and a thematic approach was used to code and inductively analyse the data. We used the Consolidated Framework for Implementation Research to identify and understand the determinants to a successful implementation of SBE. RESULTS: We interviewed 13 participants from different political and administrative levels. We found that the participants had limited knowledge about SBE, which highlighted a disconnection between these leaders and the educational environment. This was further compounded by a lack of effective communication and inadequate information dissemination between simulation centres and higher-level organisations. While participants recognised the benefits of SBE for doctors in training, they expressed concerns about the implementation given the already strained health care system and limited resources. The need for evidence, particularly in the context of patient safety, was emphasised to facilitate SBE implementation. Although participants supported the implementation of SBE, it was unclear who should initiate action. DISCUSSION: This study highlighted the perspectives of top-level health care leaders regarding SBE and identified the determinant factors for a successful implementation. Effective communication channels are crucial to enhance collaborations and reduce the disconnection between the different health care organisational levels. Strategic implementation processes, including the roles and responsibilities, should be defined and established. These will inform decisions regarding the implementation strategies to effectively integrate SBE into the residency training curricula.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".