Comparing Resource Management Skills in a High- versus Low-Resource Simulation Scenario: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Low-resource environments, such as those found in humanitarian crises, pose significant challenges to the provision of proper medical treatment. While the lack of training of health providers to such settings has been well-acknowledged in literature, there has yet to be any scientific evidence for this phenomenon. METHODS: This pilot study utilized a randomized crossover experimental design to examine the effects of high- versus low-resource simulated scenarios of a resuscitation of a critically ill obstetric patient on a medical doctors' performance and inter-personal skills. Ten senior residents (fifth-year post-graduate) of the Maggiore Hospital School of Medicine (Novara, NO, Italy) were included in the study. RESULTS: Overall performance score for the high-resource setting was 5.2, as opposed to only 2.3 for the low-resource setting. The mean effect size for the overall score was 2.9 (95% CI, 1.7-4.0; P <.001). The results suggest a significant decrease in both technical (medical) and non-technical skills, such as leadership, problem solving, situation awareness, resource utilization, and communication in the low-resource environment setting. The latter finding is of special important since it was yet to be reported. CONCLUSIONS: This pilot study suggests that untrained physicians in low-resource environments may experience a considerable setback not only to their professional performance, but also to their interpersonal skills, when deployed ill-prepared to humanitarian missions. Consequently, this may endanger the health of local populations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".