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Record W2793413038 · doi:10.1097/ceh.0000000000000189

Innovative Multimodal Training Program for Family Physicians Leads to Positive Outcomes Among Their HIV-Positive Patients

2018· article· en· W2793413038 on OpenAlexaff
Helen H. Kang, Zishan Cui, Jason Chia, Amanda Khorsandi Zardoshti, Rolando Barrios, Viviane D. Lima, Silvia Guillemi

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsAIDS Vancouver
Fundersnot available
KeywordsMedicineFamily medicineHuman immunodeficiency virus (HIV)Clinical PracticeAntiretroviral therapyMEDLINEViral load

Abstract

fetched live from OpenAlex

CME programs can increase physicians' uptake and adherence to clinical guidelines for chronic diseases. We developed an intensive multimodal training program for family physicians to increase their competency in the management and treatment of HIV, through group learning and via close interactions with expert clinicians in HIV. We trained 51 physicians from September 2010 to June 2015 and compared their adherence to clinical guidelines 1 year before and 1 year after the program. We observed significant increases in the physicians' HIV-related clinical competencies, in accordance with clinical guidelines, and an increase in the number of HIV-positive patients seen by these physicians and the number of combination antiretroviral therapies prescribed by these physicians. By combining various pedagogical approaches, as well as creating and encouraging communities of practice, we were able to make a durable impact on physician performance and patient-specific outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.041
GPT teacher head0.451
Teacher spread0.410 · 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
Published2018
Admission routes1
Has abstractyes

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