The knowledge needs for Canadian paediatric emergency physicians in the diagnosis and management of tropical diseases: A national physician survey
Bibliographic record
Abstract
OBJECTIVES: To assess the knowledge gaps and need for continuing medical education (CME) resources for Canadian paediatric emergency department (PED) physician management of common tropical diseases. METHODS: A cross-sectional survey study of Canadian PED was performed from May to July 2017 using the Pediatric Emergency Research Canada (PERC) database. RESULTS: The response rate was 56.4% (133/236). The mean performance on the case-based vignettes identifying clinical presentation of tropical illnesses ranged from 59.9% to 76.0%, with only 15.8% (n=21) to 31.1% (n=42) of participants scoring maximum points. Those who 'always' asked about fever performed better than those who only 'sometimes' asked (40.4% versus 23.8%). For management cases, the majority of the participants (59.4% to 89.5%) were able to interpret investigations; however, many were unsure of subsequent actions relating to initial treatment, discharge instructions, and reporting requirements. Many would consult infectious diseases (87.8% to 99.3%). Fifty-three per cent of the participants reported a low comfort level in diagnosing or managing these patients. They rated the importance of CME materials with a median of 50/100, via various modalities such as case studies (71.9%), emphasizing a need for PED-specific content. CONCLUSION: This study identified a knowledge gap in the recognition and management of pediatric tropical diseases by Canadian PED physicians. There is a need for formal CME materials to supplement physician practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".