Hyperbaric medicine in Canadian undergraduate medical school curriculum
Bibliographic record
Abstract
Introduction: Hyperbaric oxygen treatment (HBOT) has fourteen approved indications in the management of acute and chronic diseases in various medical specialties. However, lack of physician knowledge and exposure to hyperbaric medicine may hinder the ability of patients to access this treatment option for approved indications. We aimed to determine the prevalence and nature of HBOT-related learning objectives in Canadian undergraduate medical education programs. Methods: Pre-clerkship and clerkship learning objectives from responding Canadian medical schools' curricula were reviewed. These were acquired through the school websites or by emailing the faculties. Descriptive statistics were used to summarise the number of hyperbaric medicine objectives taught in Canadian medical schools, and within each institution. Results: Learning objectives from seven of the 17 Canadian medical schools were received and reviewed. From the curriculum of the responding schools, only one objective was found to be related to hyperbaric medicine. Hyperbaric medicine was absent from the other six schools' objectives. Conclusions: Based on the responding Canadian medical schools, hyperbaric medicine objectives were mostly absent from undergraduate medical curricula. These findings illustrate a possible gap in HBOT education and the need for discussion regarding the design and implementation of HBOT educational initiatives in medical training.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".