Pre-Departure Curriculum and Its Impact on Ethical and Reflexive Experiential Learning and Cultural Humility
Bibliographic record
Abstract
Introduction Cross-cultural experiential learning programs create opportunities for students to engage with diverse communities through ethical and reflexive practices. Inadequate preparation may reduce cultural awareness and perpetuate harmful stereotypes, colonial attitudes, and Eurocentrism. Pre-departure training serves as a critical foundation for equipping students with the necessary skills to critically analyze dominant knowledge systems, navigate ethical dilemmas, and approach cultural differences with humility. However, there is a need to assess whether the current pre-departure training effectively fosters cultural humility and ethical engagement, as well as to identify areas for refinement that can enhance the curriculum’s relevance and impact over time. The present investigation examines whether pre-departure training supports students in developing cultural humility, engaging ethically with local communities, and enhancing self-awareness throughout the learning experience. MethodsA mixed-methods design was employed, incorporating pre- and post-trip surveys to capture quantitative shifts in students’ self-assessed readiness and qualitative reflections on their evolving perspectives. The pre-departure curriculum included interactive sessions on Eurocentric narratives, ethical engagement, and critical self-reflection. Participants ranked curriculum components, provided open-ended feedback on areas for improvement, and discussed their sense of positionality and privilege. Post-trip surveys examined changes in cultural humility and ethical reflexivity, reflecting the degree to which on-site immersion complemented the preparatory training. Quantitative data were analyzed through paired t-tests, and qualitative responses were examined using thematic analysis. Implications This research can support the ongoing refinement of HSCI 595’s curriculum by integrating student feedback to enhance its relevance and effectiveness. An effective pre-departure curriculum that prioritizes anti-colonial perspectives, cultural humility, and ethical learning can enable students to critically analyze Eurocentric assumptions. Encouraging students to critically reflect on their personal biases, positionality, and potential ethical dilemmas strengthens their global health engagement, cultural humility, and awareness of power imbalances and diverse knowledge systems.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".