Development of the specialty of emergency medicine in Israel: comparison with the UK and US models
Bibliographic record
Abstract
OBJECTIVES: To describe the development of emergency medicine (EM) in Israel and review the specific problems faced by the discipline and describe the solutions that were found. METHODS: A comprehensive literature search was conducted for data on development of EM in the UK and in North America, and the personal knowledge of two of the authors (PH and YW) was used in preparing the article. RESULTS: There are differences in development of EM between Israel and the UK/US models. In Israel the specialty developed within the context of established high quality clinical practice and consequently it met resistance from the system, which did not wish to invest in what it felt might be marginal improvements in patient care. The economics of Israeli medicine also dictated that EM be made into a super-specialty rather than a primary specialty. Certified specialists from family medicine, paediatrics, internal medicine, general surgery, anaesthesia, and orthopaedic surgery can access training positions in EM. Currently there are seven active EM programmes of 2.5 years duration and 16 residents. The curriculum is flexible and a national certification examination is being developed. CONCLUSIONS: Development of EM can and should take different paths according to the specific local needs and realities. There is no single ideal model suitable for all circumstances. The practice of clinical EM in Israel is comparable with that of any developed country and daily progress is being made in the academic areas of teaching and research. There are worldwide similarities in the process of developing EM as a distinct discipline.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".