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Record W2087143139 · doi:10.3928/00220124-20100401-06

Using Audience Response Technology in Hospital Education Programs

2010· article· en· W2087143139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsAudience responseAudience participationMedical educationPsychologyService (business)Survey instrumentTarget audienceMultimediaComputer scienceMedicineApplied psychologyAdvertisingMarketing

Abstract

fetched live from OpenAlex

An audience response system (ARS) is an interactive teaching tool that permits an instructor to poll an audience, either anonymously or in a tracked manner, in response to questions. The instructor can then display the responses to the audience. An ARS can be used in hospital-based education programs to assess group learning. The instructor receives immediate feedback that allows review of concepts that were not grasped by the majority of students. This article reviews systems currently on the market and offers tips for choosing an ARS for hospital-based use. Survey data of nurses attending in-service education sessions show that participants overwhelmingly favor the use of an ARS and the nonthreatening learning environment that these systems create. Instructor survey data show positive responses regarding the benefits of ARS use in hospital-based education programs.

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.

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)0.011
metaresearch head score (Gemma)0.004
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 score0.731
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.437
Teacher spread0.418 · 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