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An Experience With an Online Learning Environment to Support a Change in Practice in an Emergency Department

2004· article· en· W2043345655 on OpenAlexaff
Janet Curran, Shauna Best

Bibliographic record

VenueCIN Computers Informatics Nursing · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsNova Scotia Health AuthorityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsPresentation (obstetrics)Emergency departmentSet (abstract data type)Plan (archaeology)Work (physics)Knowledge managementExperiential learningMedical educationComputer sciencePsychologyMedicineNursingEngineeringPedagogy

Abstract

fetched live from OpenAlex

Providing continuing education to support a change in practice for a busy Emergency Department poses a challenge. Factors such as shift work, high patient acuity, and unpredictable patient flow create barriers to traditional methods of delivery of a comprehensive educational experience. This article describes an experience with introducing a change in practice using an innovative Web-based delivery plan. Specific strategies were employed to address presentation of content, application of knowledge, establishment of a shared understanding, and enhancement of communication opportunities. The Web-based learning environment proved to be a successful means of providing nurses with a collaborative learning experience around a new practice issue. This experience also highlighted the need for a new skill set for learners and educators using online learning technologies.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.374
Teacher spread0.339 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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