The use of information and communications technologies in the delivery of interprofessional education: A review of evaluation outcome levels
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".