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Record W1971468974 · doi:10.12927/hcq..18751

Evidence-Based Strategies to Improve Immunization Compliance of Postgraduate Medical Trainees at Large Academic-Medical Facilities

2007· article· en· W1971468974 on OpenAlexaffabout
Thirumagal Kanagasabai, L. Muharuma, Joy McGuire, Melanie Russell, Mary Vearncombe, Murray B. Urowitz

Bibliographic record

VenueHealthcare Quarterly · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmunizationMedicineMeaslesAgency (philosophy)Public healthFamily medicineHealth careCompliance (psychology)RubellaMedical educationVaccinationNursingPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The purpose of this evaluation is to assess the effectiveness of the modifications made by the University of Toronto Postgraduate Medical Education to improve medical trainee compliance with the immunization standards set forth in national guidelines, provincial regulations and protocols and university policy. Trainee compliance with immunization requirements were evaluated as of January 2003, 2004 and 2005. Statistically significant increases in compliance rates for all required immunizations--hepatitis B virus, measles, rubella and chicken pox--and tuberculosis skin tests were observed. University of Toronto postgraduate medical trainees are now highly compliant with the Hospital Management Regulation 965 of the Ontario Public Hospitals Act, Canadian Immunization Guide, Public Health Agency of Canada guidelines for prevention and control of occupational infections in healthcare and the University of Toronto Faculty of Medicine immunization policy.

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)0.022
metaresearch head score (Gemma)0.091
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.080
GPT teacher head0.393
Teacher spread0.313 · 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

Citations3
Published2007
Admission routes2
Has abstractyes

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