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Record W2125765946 · doi:10.1177/1460458207079834

Evaluation of an online discussion forum for emergency practitioners

2007· article· en· W2125765946 on OpenAlexafffund
Janet Curran, Syed Sibte Raza Abidi

Bibliographic record

VenueHealth Informatics Journal · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health CentreCapital District Health Authority
FundersMichael Smith Health Research BC
KeywordsOnline discussionWorkflowEmergency departmentAsynchronous communicationPerspective (graphical)Social mediaMedical educationQuality (philosophy)Online forumKnowledge managementPsychologyComputer scienceMedicineNursingInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

Knowledge is a critical element in the delivery of quality healthcare. In a busy emergency department (ED) clinicians attempting clinically relevant discussion with their peers face multiple interruptions and a lack of sustained meaningful interactions. Information and communication technologies such as online discussion forums enable practitioners to share practice knowledge at times that fit into their daily workflow. We conducted an experiment in which we provided emergency clinicians with access to an asynchronous discussion forum as a medium to support development of an online social network for information exchange. The outcomes were evaluated using a social network perspective to better understand the knowledge seeking and sharing behaviors among rural and urban emergency practitioners participating in the online discussion forum. The online discussion forum created an opportunity for emergency practitioners from multiple ED sites to engage in dialogue around topics that were relevant to their practice learning 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 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)0.039
metaresearch head score (Gemma)0.138
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 score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.109
GPT teacher head0.400
Teacher spread0.291 · 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

Citations29
Published2007
Admission routes2
Has abstractyes

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