Training of Icelandic rural doctors in managing trauma and acute illness
Bibliographic record
Abstract
INTRODUCTION: Rural medicine is in many ways different from urban primary care. In addition to providing primary care for a population, the rural doctor is tasked with the initial evaluation and stabilization of all emergencies usually managed by an Emergency Department in urban areas. The goal of this study was to assess rural doctors' in Iceland attendance of courses in Emergency Medicine (EM), how rural doctors grade their own ability to respond to emergencies and evaluate their Continuous Medical Education (CME) within the field of EM. MATERIALS AND METHODS: In this descriptive cross-sectional study, all rural general practitioners (GP) in Iceland with at least two years of experience post foundation training and who practiced at least a quarter of every year outside the capital area were surveyed using an electronic questionnaire. T-test and qi-square test were used for analysis and significance determined if p<0.05. RESULTS: The survey was sent to 84 doctors with 47 (56%) completing the survey. Over 90% of the participants reported having completed a course in Advanced Life Support (ALS) but only 18% had completed a course in prehospital EM specifically designed for this group of doctors. Over half of the participants considered themselves to have good training to perform 7 out of 11 surveyed emergency procedures. Over 40% of participants considered it necessary to improve their CME in 7 out of 10 categories of EM. The majority of rural GPs considered shortage of doctors in the rural environment a significant factor limiting their CME. CONCLUSIONS: The majority of rural doctors in Iceland consider themselves to have a good training to provide initial EM care in their community. Efforts to improve their training in this field of medicine should focus on scene safety and working in the prehospital setting, pediatrics, labor and deliveries and gynecological emergencies. Rural doctors need to have access to appropriate EM training courses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".