Exploring the Role of Experiential Learning in Tanzania on the Understanding of One Health
Bibliographic record
Abstract
The One Health framework recognizes the unifying interconnectedness of human, animal, and environmental health, emphasizing the need for interdisciplinary collaboration to address global health challenges. Within this framework, cultural determinants of health including education, beliefs, values, and traditional practices play a critical role in shaping cultural norms, healthcare delivery, and policies. Understanding these determinants is essential for designing effective public health strategies that respect cultural contexts. This study introduces the first iteration of the experiential learning course HSCI 595, designed to build cultural competency and humility to foster health equity learning through direct community engagement. Featuring pre-departure training, reflective assessments, and a 20-day educational trip to Tanzania, this course integrates service-learning experiences at Pamoja Tunaweza Women’s Centre in Tanzania. The course equips students with the skills needed to leverage One Health principles into applications of holistic healthcare. The three primary objectives are to explore how an experiential learning opportunity influences One Health conceptual understanding; how social and health-based activities impact learning and the perceived application of One Health in human health careers; and how experiential learning activities shape understanding of the role of non-human and ecological spheres. A pre/post-survey with quantitative and qualitative metrics will be used to assess changes in students’ understanding of One Health. The study will compare results between students enrolled in HSCI 595 and a cohort of students participating in a theoretical setting to learn about the One Health framework. By integrating experiential learning, this study provides insights into the effectiveness of immersive educational approaches in global health. The findings will contribute to improving curriculum design and preparing future professionals to address complex health challenges through a One Health perspective.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".