Exploring Factors That Influence Student Engagement in Community-Engaged Learning Activities Within a Pharmacy Context
Bibliographic record
Abstract
Objective. To investigate and identify factors that enhance and restrict student engagement in mandatory and voluntary community-engaged learning (CEL) activities. Methods. A phenomenological study utilizing semi-structured interviews was conducted, exploring students’ motivations and barriers faced in their mandatory CEL course and voluntary CEL activities (eg, community outreach). Fifteen students were randomly selected to participate in interviews. Student responses were analyzed using qualitative thematic analysis. Results. Primary factors motivating student engagement in mandatory CEL included having structured learning activities for students and incorporating reflective learning. Motivating factors for students participating in voluntary CEL included personal interest in the topic, convenient location and time of activity, opportunity for career development, and advocating for the pharmacy profession. Overlapping motivations for both mandatory and voluntary CEL included having a better understanding and broader perspective of the diverse populations in the community and imparting a positive impact. Common barriers identified included having limited information about student responsibilities, limited student role, and feeling unconfident or unprepared. Conclusion. Students perceive benefits from both mandatory and voluntary CEL activities. However, opportunities exist for identifying and managing barriers to enhance student engagement in CEL within a pharmacy program and to further refine the use of learning tools, such as critical reflection, that were identified through this study to have contributed to student engagement with CEL.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".