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Record W1758934566 · doi:10.3109/13561820.2015.1021002

The use of information and communications technologies in the delivery of interprofessional education: A review of evaluation outcome levels

2015· review· en· W1758934566 on OpenAlexaff
Vernon Curran, Adam Reid, Pamela Reis, Shelley Doucet, Sheri Price, Lindsay Alcock, Shari Fitzgerald

Bibliographic record

VenueJournal of Interprofessional Care · 2015
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsDalhousie UniversityUniversity of New BrunswickMemorial University of Newfoundland
Fundersnot available
KeywordsModalitiesInformation and Communications TechnologyTypologyInterprofessional educationMedical educationMedicinePsychologyHealth carePolitical scienceSociology

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) in health and human services educational and clinical settings has proliferated internationally. The use of information and communication technologies (ICTs) in the facilitation of interprofessional learning is also growing, yet reviews of the effectiveness of ICTs in the delivery of pre- and/or post-licensure IPE have been limited. The current study's purpose was to review the evaluation outcomes of IPE initiatives delivered using ICTs. Relevant electronic databases and journals from 1996 to 2013 were searched. Studies which evaluated the effectiveness of an IPE intervention using ICTs were included and analyzed using the Barr et al. modified Kirkpatrick educational outcomes typology. Fifty-five studies were identified and a majority reported evaluation findings at the level 1 (reaction/satisfaction). Analysis revealed that learners react favorably to the use of ICTs in the delivery of IPE, and ICT-mediated IPE can lead to positive attitudinal and knowledge change. A majority of the studies reported positive evaluation outcomes at the learner satisfaction level, with the use of web-based learning modalities. The limited number of studies at other levels of the outcomes typology and deficiencies in study designs indicate the need for more rigorous evaluation of outcomes in ICT-mediated IPE.

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)0.006
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: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.278
GPT teacher head0.571
Teacher spread0.292 · 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
GenreReview

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

Citations63
Published2015
Admission routes1
Has abstractyes

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