Curricular Factors that Unintentionally Affect Learning in a Community-Based Interprofessional Education Program: The Student Perspective
Bibliographic record
Abstract
Background: The Dalhousie Health Mentors Program (DHMP) is a community-based, pre-licensure interprofessional education initiative that aims to prepare health professional students for collaborative practice in the care of patients with chronic conditions. This program evaluation explores the students’ 1) learning and plans to incorporate skills into future practice; 2) ratings of program content, delivery, and assignments; 3) perspectives of curricular factors that inadvertently acted as barriers to learning; and 4) program improvement suggestions.Methods: All students (N = 745) from the 16 participating health programs were invited to complete an online mixed methods program evaluation survey at the conclusion of the 2012–2013 DHMP. A total of 295 students (40% response rate) responded to the Likert-type questions analyzed using descriptive and non-parametric statistics. Of these students, 204 (69%) provided responses to 10 open-ended questions, which were analyzed thematically.Findings: While the majority of respondents agreed that they achieved the DHMP learning objectives, the mixed-methods approach identified curriculum integration, team composition, and effectiveness of learning assignments as factors that unintentionally acted as barriers to learning, with three key student recommendations for program improvement.Conclusions: Educators and program planners need to be aware that even well-intended learning activities may result in unintended experiences that hamper interprofessional learning.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".