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Record W2751408670 · doi:10.4088/pcc.17br02167

The State of Sleep Medicine Education in North American Psychiatry Residency Training Programs in 2013: Chief Resident’s Perspective

2017· article· en· W2751408670 on OpenAlexaff
Imran Khawaja, Patricia Dickmann, Thomas D. Hurwitz, Paul Thuras, Robert E. Feinstein, Alan B. Douglass, Elliott Kyung Lee

Bibliographic record

VenueThe Primary Care Companion For CNS Disorders · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSleep medicineResidency trainingMedicineObstructive sleep apneaSleep (system call)Family medicinePsychiatryGraduate medical educationMedical educationSleep disorderInsomniaContinuing educationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the current state of sleep medicine educational resources and training offered by North American psychiatry residency programs. METHODS: In June 2013, a 9-item peer-reviewed Sleep Medicine Training Survey was administered to 39 chief residents of psychiatry residency training programs during a meeting in New York. RESULTS: Thirty-four percent of the participating programs offered an elective rotation in sleep medicine. A variety of innovative approaches for teaching sleep medicine were noted. The majority of the chief residents felt comfortable screening patients for obstructive sleep apnea (72%), half felt comfortable screening for restless legs syndrome (53%), and fewer than half were comfortable screening for other sleep disorders (47%). CONCLUSIONS: This is the first report in the last decade to provide any analysis of current sleep medicine training in North American psychiatry residency training programs. These data indicate that sleep medicine education in psychiatry residency programs is possibly in decline.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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