A Phenomenological Account of Women Graduate Students: Lived Experience of Challenges in Global Public Health Practice
Bibliographic record
Abstract
During the last two decades academic global public health grew dramatically, including higher student demand to participate in internationally-based fieldwork. As a foundational space for graduate level training, practice, and research activities, the university plays a primary and significant role in the formation and future practice of students. While academic research and practice opportunities within international settings may provide unique experiences for U.S. and Canadian graduate students, it remains an overlooked area of inquiry. At times, these experiences can be intensely challenging or distressing, with additional burdens experienced by women. This study aims to gain insight into the lived experience of Canadian and U.S. women graduate students participating in global public health practice, and how they experience this phenomenon. This study is situated within Max van Manen’s qualitative methodology, phenomenology of practice. This hermeneutic approach is aimed at meaning-making through emotional, embodied, existential, and pathic ways of knowing. Seven women participated in 18 in-depth phenomenological interviews, while 18 women completed a guided writing exercise. Data creation methods were designed to capture lived experience descriptions of global public health practice. Key themes discussed in this dissertation include sexual and gender-based violence and harassment, discomfort with privilege, mental health challenges (specifically depression), and not being heard. Further findings focused on the burden of hidden labor performed by women to keep themselves safe from sexual and gender-based violence, including discussion on travel safety and transportation, witnessing or experiencing violence, lack of preparedness or follow-up, financial burdens and stress, and support networks and peer relationships as protective. The experience of participating in global public health practice is of particular consequence to the training, education, and future practice of graduate students. This study holds the potential to positively affect an individual’s experience, and thus practice, through offering new meaning structures, language for an unfamiliar experience, or ways to describe, conceive of, and respond to global public health fieldwork. Furthermore, by dedicating space to this phenomenon and giving voice to women graduate students’ lived experience, public health institutions may be better able to recognize, validate, respond to, and support students participating in global public health practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".