Interprofessional education focused on medication safety: a systematic review
Bibliographic record
Abstract
Safe medication use necessitates interprofessional working, with calls to enhance interprofessional education (IPE) focusing on medication safety (MS) in healthcare professional (HCP) curricula. Little is known about the design, delivery or evaluation of such activities. This systematic literature review describes MS-focused IPE activities in pre-qualification HCP programmes. MedLine, EMBASE, CINAHL and ERIC were searched, relevant studies identified and data extracted. The McGill Mixed Methods Appraisal Tool was employed. The 3P (presage-process-product) theory structured deductive analysis. Thirty-one studies were included, reporting on 30 activities, mostly undertaken in North America or United Kingdom. Presage/Design: Most reported activities involved pharmacy, nursing, medical or physician assistant students learning with one or more other HCP group. Few studies matched student groups' skills or experiences. Few studies reported theoretical underpinnings. Process/Delivery: Multiple pedagogical approaches were employed, mostly social construction, and low- and high-fidelity simulation-based learning. Few studies reported learning outcomes or summative assessment, more reported formative assessment. Product/evaluation: Outcomes measured were learners' opinions, satisfaction or attitudes toward interprofessional working and findings were generally positive. Few studies reported on student development or outcomes specific to medication safety. Lack of integration of qualitative/quantitative components of mixed methods studies and limited outcome measurements' validity or reliability weakened study quality. MS-focused IPE for pre-qualification HCPs is well received by students. Design of future activities could be enhanced by employing theory and ensuring matching of students' and groups' skills, professional identity and learner attributes to enhance learning in an interprofessional setting. Future delivery should embed MS-focused IPE into the standard curricula to optimize constructive alignment, learner engagement, quality and drive development. The required skillset in pre-qualification HCP programmes to facilitate future safe medication practice, together with the associated learning outcomes and assessment approaches, should be defined. The quality of scholarly studies examining these activities needs improvement.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".