Bibliographic record
Abstract
Aerospace medicine training is often difficult to obtain outside of military education streams. Undergraduate medical trainees and residents may undertake training opportunities, but often have trouble locating programs and/or receiving credit for their experiences and learning. In many countries, no formal aerospace medicine training program or pathway exists and trainees must search out opportunities on their own. Canada is used as an example of a country which, until recently, had no defined civilian aerospace medicine training program or credentialing pathway. Recent development of a Diploma in Aerospace Medicine certified by the Royal College of Physicians and Surgeons now outlines a series of competencies for trainees and medical professionals seeking advancement in aerospace medicine. Growth of the aviation and aerospace fields will require more training opportunities and more aerospace medicine professionals to support the increased number of aviators and the spacefaring population. This will be particularly important as commercial space companies develop the potential for civilian spaceflight. While few opportunities exist for training, we highlight the major aerospace medicine training opportunities that have been recently available to Canadians. It is our hope that highlighting previous and current opportunities may aid in the development of a formal training program leading to certification in aerospace medicine for Canadians and act as an example for other nations.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".