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Record W2085486204 · doi:10.1097/jom.0b013e318159b48f

Occupational Medicine Residency Graduate Survey: Assessment of Training Programs and Core Competencies

2007· article· en· W2085486204 on OpenAlexaboutno aff
Beth A. Baker, Sharda Katyal, Ian A. Greaves, Heidi Roeber Rice, Edward A. Emmett, John D. Meyer, Wei He

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
FundersNational Institute for Occupational Safety and HealthMidwest Center for Occupational Health and Safety
KeywordsResidency trainingMedicineCertificationCore competencyFamily medicineMedical educationGraduate medical educationBoard certificationCurriculumTraining (meteorology)PsychologyContinuing educationAccreditationManagementPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study provides insight into Occupational Medicine (OM) residency graduates and how residency programs are meeting their education goals. METHODS: A survey of graduates from nine OM residency program was performed to evaluate the effectiveness of OM residency training in the United States and Canada. RESULTS: Eighty percent of the OM residency graduates were currently practicing OM. Three-quarters worked in clinical practice for a mean of 20 hr/wk. Other activities varied and included management, teaching and consulting. Ninety-five percent were satisfied with their OM residency training. The competencies acquired were mostly ranked highly as practice requisites, although preparation in clinical OM might be better emphasized in training. Recent OM residency graduates were more likely to be board-certified in OM than other American College of Occupational and Environmental Medicine physician members (73% vs 41%). CONCLUSIONS: OM residency graduates over the past 10 years were highly satisfied with OM residency training, with the training generally meeting practice needs.

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)0.002
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 score0.014
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.170
GPT teacher head0.391
Teacher spread0.221 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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