Implementing longitudinal integrated curricula: Systematic review of barriers and facilitators
Bibliographic record
Abstract
PURPOSE: The increase of longitudinal integrated curricula in medical schools worldwide represents the shift towards an outcome-oriented education. This novel model allows comprehensive student-patient interactions over time and integrates the educational content across disciplines. According to quantitative research, students, patients, doctors and communities benefit from this educational model in terms of participant satisfaction, learning outcomes and clinician recruitment. However, quantitative research does not provide detailed information on programme implementation processes. Therefore, this review aims to summarise facilitators and barriers of programme implementation reported in qualitative and mixed methods studies. METHOD: The authors reviewed the literature about facilitators and barriers for the implementation of longitudinal integrated curricula in undergraduate medical education programmes. The systematic search was conducted in MEDLINE, Embase and PsycINFO on 2 December 2019. The authors used the CASP checklist for qualitative research for the critical appraisal and summarised the results across studies using thematic content analysis. RESULTS: The authors screened 1682 reports. Twenty studies examining 17 different curricula met the inclusion criteria. Most curricula were implemented in the United States (n = 6/17), Australia (n = 5/17) or Canada (n = 4/17). Programme implementation is facilitated and hampered by its educational components (eg continuity of supervision, safe learning environments), organisational structures (eg community involvement) and participating students' and staff' motivation and personality. The critical appraisal revealed that several studies lacked transparent documentation and adequate reflection on the researcher-participant relationship (n = 20/20), data collection instruments (n = 12/20) and recruitment strategy (n = 4/20). CONCLUSIONS: The authors derived practical recommendations for the implementation of undergraduate, patient-centred, integrated medical curricula. Programme managers need to define and communicate common objectives with all participants. They should clarify the implementation of the objectives in all processes in a transparent and structured manner. Considering reporting guidelines, future studies in this field should document more transparently the methods used to gain qualitative insights and the researchers' personal involvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".