Assessing Primary Care Trainee Comfort in the Diagnosis and Management of Thermal Injuries
Bibliographic record
Abstract
Thermal injuries are common and the majority will initially present to primary care physicians. Despite being a part of the objectives of training in family medicine (FM) and emergency medicine (EM), previous study has shown that in practice, gaps exist in the delivery of care. An electronic survey was sent to all FM/EM trainees at our university for the 2014 to 2015 academic year. Plastic Surgery trainees were included as a control group. Demographics and educational/clinical experience were assessed. Trainee comfort was measured on a five-point Likert scale across 15 domains related to thermal injuries. Preferences for educational interventions were also ranked. Descriptive statistics and the Kruskal-Wallis test were used (P < .05 considered significant). The survey response rate was 27.4% (117/427). FM and EM (CCFP and Royal College) trainees estimated a median 0, 1, and 2 hours of total didactic instruction, respectively. During that academic year, FM and EM (CCFP and Royal College) trainees cared for a median 1, 4, and 5 patients, respectively. Significant differences were noted in comfort levels across all 15 domains when compared with plastic surgery trainees. Preferences for educational interventions were ranked, with clinical rotations and traditional lecture scoring the highest. Primary care trainees are not comfortable in the diagnosis and management of thermal injuries. This may be attributed to limited clinical exposure and teaching during their postgraduate training. There exists an opportunity for specialists in burn care to collaborate with primary care training programs and deliver an educational intervention with the aim of long-lasting quality improvement.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".