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Record W4382181330 · doi:10.28920/dhm53.2.138-141

Hyperbaric medicine in Canadian undergraduate medical school curriculum

2023· article· en· W4382181330 on OpenAlexaffabout
Zoé Talbot, Alex Lee, Sylvain Boet

Bibliographic record

VenueDiving and Hyperbaric Medicine Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsInstitut du Savoir MontfortOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsHyperbaric oxygenCurriculumMedicineMedical schoolMedical educationFamily medicineIntensive care medicinePsychologySurgeryPedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.017
GPT teacher head0.300
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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