General emergency medicine skills for rural doctors
Bibliographic record
Abstract
Context and setting Rural doctors who practise emergency medicine need to be appropriately skilled to handle medical emergencies. Why the idea was necessary Doctors in remote areas have difficulty accessing relevant programmes in emergency medicine. What was done Alberta's Rural Physician Action Plan (RPAP) established a working committee consisting of RPAP's programme manager, 2 skills brokers (both rural doctors), rural and tertiary emergency doctors and the director of the Shock Trauma Air Rescue Society (STARS). A needs assessment carried out in 2001 identified a rural knowledge gap surrounding certain skills in emergency medicine that were not covered in current continuing medical education programmes. To address this deficiency, the committee developed a programme for rural doctors designated General Emergency Medicine Skills (GEMS), which included both cognitive and practice components. The cognitive component utilised a CD ROM multimedia approach, allowing doctors to learn at their own pace and locale. Four modules were produced: Preparation for Transport; C-Spine/CT-Head; Rapid Sequence Intubation, and Central Venous Access. Each requires about 1.5 hours to complete. Following completion, STARS scheduled a visit to the doctors' rural hospital with their mobile human patient simulator (HPS). This allowed the entire emergency team to practise scenarios developed by the STARS HPS clinicians using information derived directly from the CD ROM modules. Participants received instant feedback and exposure to new techniques and equipment. Doctors were able to enhance their skills further with the tertiary emergency doctors at the University of Calgary Medical School's anatomy laboratory. Participants in GEMS register online and each doctor who completes all CD modules and the HPS component receives a $1000 honorarium. Completion of a pre/post online knowledge test qualifies for continuing medical education credits through the College of Family Physicians of Canada Mainpro-C programme. During the pilot year, which began in September 2004, 66 sets of modules were distributed. A programme evaluation survey was distributed. Evaluation of results and impact All 16 doctors who completed the pilot programme returned a survey. The educational value of each module was highly rated (6.1/7–6.5/7). Of the 79 specific learning objectives participants listed, expectations for 70 (89%) were either completely fulfilled or exceeded. The modules facilitated: maintenance of current knowledge (6.1/7); new knowledge acquisition (5.5/7), and confidence in dealing with emergencies (5.4/7). All participants utilised the STARS HPS. The anatomy laboratory component was not accessed. Participants felt the STARS HPS: reinforced module knowledge (6.3/7); allowed practice of the skills presented in modules (6.3/7), and was essential to benefit fully from the modules (6.2/7). Regarding programme administration, participants agreed that: the application process was easy (6.1/7); terms/conditions were reasonable (6.3/7); the honorarium of $1000 was fair (6.3/7), and general programme administration was good (6.5/7). Overall, GEMS has positively affected participants' delivery of emergency medicine (5.7/7) and willingness to continue rural emergency medicine (5.5/7). Feedback has initiated minor changes to facilitate participant follow-up and completion. GEMS can help rural doctors to not only learn and practise emergency medicine skills, but to do so with confidence. Interactive training can be delivered at the rural site.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".