Translating the healthcare simulation standards of best practice for low-resource settings: Operational strategies in health professions education
Bibliographic record
Abstract
Background: Healthcare simulation is increasingly recognized as a transformative educational methodology. The International Nursing Association for Clinical Simulation and Learning (INACSL) developed the Healthcare Simulation Standards of Best Practice (HSSOBP) to guide high-quality simulation-based education (SBE) however, implementing these in resource-constrained settings poses challenges due to economic and cultural barriers.Methods: A team from universities in Uganda, USA and Eswatini collaborated to contextualize and translate the HSSOBP for low-resource healthcare education settings. Using independent reviews and group discussions, the team analysed each standard for operational feasibility, contextual challenges, and resource-aligned solutions.Results: Adaptation included enhancing organizational readiness through faculty and staff capacitation on SBE, simplifying operational strategies, using low-fidelity equipment, interdisciplinary collaboration and mobilizing local resource structures to support and sustain simulation programmes.Conclusions: The translated HSSOBP guide the integration of SBE pedagogy in low-resource settings. This framework suggests practical and contextualised strategies without compromising the quality of education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.145 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".