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Record W4391219338 · doi:10.31254/sportmed.7202

An In-Depth Analysis of Canadian Medical Students and Physicians Concussion Knowledge: A Cross-Sectional Study

2023· article· en· W4391219338 on OpenAlexaffabout
Scott D. Bray, Shannon Hart, Ryan Kelly, Ryan Murray, Anna P. Nippard, Jared M. Ryan, R. J. Avery

Bibliographic record

VenueInternational Journal of Sport Exercise and Health Research · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConcussionCross-sectional studyPsychologyMedical educationMedicineFamily medicineInjury preventionMedical emergencyPoison control

Abstract

fetched live from OpenAlex

Background: Research has shown that concussion education in a proportion of Canadian medical school curriculums is lacking. The objective of this study was to measure concussion knowledge among medical trainees, while comparing the impact of lecture-based and clinical learning on their concussion knowledge scores. Methods: A validated concussion knowledge survey was distributed to MS1, MS2, MS3, and MS4 students, as well as post-graduate learners in family medicine, pediatrics, and emergency medicine. Results: Participants with clinical learning experience (MS3 + MS4 + Residents) scored significantly higher (p < 0.05) in the concussion knowledge test when compared to pre-clinical (MS1 + MS2) participants. 42.2% of participants learned about concussions from a lecture and 28.1% learned from a student organized interest group. Only 25% of participants correctly identified the red flags associated with a concussion. Conclusion: More emphasis should be placed on teaching concussion diagnosis and management in medical education.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.255
GPT teacher head0.574
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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