Bibliographic record
Abstract
Introduction: Transformative experiences have shown to be an impactful part of the development and learning of future healthcare professionals in the past. Many of these experiences have historically been obtained through service-related trips, such as Operation Smile, Medical Brigades, etc. While these trips are beneficial, they also present potential harms related to lacking community engagement, application of Western views in cultural contexts, and a lack of follow-up care in the communities after the students return to their country of origin. Methods: Students participating in the HSCI595 course which involves an experiential learning trip to Moshi, Tanzania completed mixed-methods pre-course survey, pre-trip survey, daily transformative experience journalling, and a post-trip survey. Once the pre- and post-trip data was obtained, the qualitative data was coded to determine major themes among pre-trip responses and post-trip responses separately. This qualitative and quantitative data was combined to summarize results and identify any changes in themes and correlational relationships between impactful transformative experiences and competency development. Results: By examining the specific transformative experiences that led to competency development in HSCI595, we hope to determine the factors and common themes of global health collaboration trips that led to competency development. Conclusion: The conclusion derived from this data will aid in the organization and optimization of future global health collaboration trips within the HSCI595 course, as well as providing a framework of beneficial factors for other trips as well. In the future, to compare the differences and similarities between both service-related and education-oriented trips, such as the one carried out in HSCI595, mixed-methods surveys will be used to examine competency development within focus groups from campus partners who host service-related trips, and the Queen’s student body in genera and compared to data from the HSCI 595 group.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".