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Record W2125789244 · doi:10.3109/02770900009055447

Implementing Continuing Education Strategies for Family Physicians to Enhance Asthma Patients' Quality of Life

2000· article· en· W2125789244 on OpenAlexaff
Paula Blackstien-Hirsch, Geoff Anderson, Lisa Cicutto, Andrew McIvor, Peter Norton

Bibliographic record

VenueJournal of Asthma · 2000
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity Health NetworkUniversity of TorontoDalhousie UniversityQueen Elizabeth II Health Sciences CentreUniversity of CalgaryInstitute for Clinical Evaluative Sciences
FundersNational Center for Advancing Translational Sciences
KeywordsAsthmaIntervention (counseling)Continuing medical educationMedicineFamily medicineQuality of life (healthcare)Academic detailingQuality managementContinuing educationService (business)NursingMedical education

Abstract

fetched live from OpenAlex

The goal of the study was to provide asthma-related continuing medical education (CME) to family physicians with the objective of improving patient outcomes. Using a quasi-experimental design in a single community, the intervention included academic detailing, a case-based workshop, newsletters, medical grand rounds, and patient-centered education materials. Outcome measures included physician participation in CME; patient self-reported quality of life, asthma knowledge, asthma self-management, medication use, and health service utilization before and after the intervention; and physician feedback. Our results indicated that 78% of family physicians participated in one or more of the CME activities. The majority of physicians provided positive feedback on the use of the intervention both from their own and their patients' perspectives. Academic detailing increased the involvement of physicians in CME. We concluded that there was a statistically significant improvement in patients' quality of life, whereas changes in patients' knowledge, behavior, and health service use were positive but not statistically significant. Methodological factors are identified that could improve the effectiveness of future studies.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.342
Teacher spread0.328 · 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 designOther design
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

Citations20
Published2000
Admission routes1
Has abstractyes

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