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Record W2125617733

Small-group CME using e-mail discussions. Can it work?

2001· article· en· W2125617733 on OpenAlexaff
Jonathan Marshall, Moira Stewart, Truls Østbye

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsThe InternetContinuing medical educationMedical educationFocus groupMedicineElectronic mailFamily medicineWork (physics)PsychologyContinuing educationWorld Wide WebComputer scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

PROBLEM BEING ADDRESSED: Traditional continuing medical education (CME) approaches do not work well in changing physicians' behaviour, but some promising strategies and technologies might help. Our program sought to meld small-group learning with an Internet e-mail approach. OBJECTIVE OF PROGRAM: In 1994, the Family medicine Education and Research Network (FERN) was developed to support on-line discussion among London, Ont, and area family physicians. To support educational, moderated case discussions using e-mail, FERN Dissemination (FERN-D) was introduced to a subgroup of participants. We hoped to increase awareness and use of evidence-based research in clinical practice and to increase use of Internet-based resources for CME. The target group was family physicians in the London area. MAIN COMPONENTS OF PROGRAM: Forty volunteers were recruited and were e-mailed one case every 2 weeks; 34 completed the study. Each case was followed by further postings and, at the end of 2 weeks, by a summary of the group's discussion. Background material for each case was researched and was evidence-based. Evaluation was conducted using preintervention and postintervention mailed surveys combined with an e-mail feedback questionnaire and a modified focus group. CONCLUSION: On-line case-based discussion is a promising strategy for encouraging family physicians to access current research. More research is needed to determine whether it can be effectively used to change physicians' practice.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.174
GPT teacher head0.385
Teacher spread0.212 · 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

Citations36
Published2001
Admission routes1
Has abstractyes

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