MétaCan
Menu
Back to cohort
Record W2327822865 · doi:10.1017/s0317167100003735

The Academic Half-Day in Canadian Neurology Residency Programs

2004· article· en· W2327822865 on OpenAlexaffvenueabout
Colin Chalk

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2004
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsAttendanceNeurologyMedicineResidency trainingFamily medicineMedical educationPsychiatryContinuing education

Abstract

fetched live from OpenAlex

BACKGROUND: The academic half-day (AHD) appears to have become widespread in Canadian neurology residency programs, but there is little published information about the structure, content, or impact of the AHD. METHODS: A written questionnaire was sent to the directors of all active Canadian adult and child neurology residency programs. RESULTS: All 21 program directors responded. An AHD was operating in 15/15 adult and 5/6 child neurology programs. The AHD typically lasts three hours, and occurs weekly, 10 months per year. Most of the weekly sessions are lectures or seminars, usually led by clinicians, with about 90% resident attendance. Course-like features (required textbook, examinations) are present in many AHDs. There is a wide range of topics, from disease pathophysiology to practice management, with considerable variation between programs. CONCLUSIONS: Almost all Canadian neurology programs now have an AHD. Academic half-days are broadly similar in content and format across the country, and residents now spend a substantial portion of their training attending the AHD. The impact of the AHD on how residency programs are organized, and on the learning, clinical work, and professional development of residents merits further study.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.510
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicHospital Admissions and OutcomesFrench-language works237,207