Witnessing the Ward: On the Emotional Labor of Doing Hospital Ethnography
Bibliographic record
Abstract
This paper examines the emotional labor performed by researchers when undertaking ethnographic research in hospitals. Drawing on emotion work theory to situate emotions at the center of qualitative and interdisciplinary research, I provide a methodological reflection based on a 20-week long ethnography at a Canadian pediatric hospital I conducted in the context of a research project examining risk communication of antimicrobial resistance. I argue that the emotional labor in which hospital ethnographers engage starts long before the fieldwork and carries on throughout the project and into the data analysis and writing of results. I divide these instances of emotional labor into four categories: gaining and maintaining access to the field site, resolving ethical concerns, managing relations with participants, and witnessing human suffering. This paper addresses a gap in the literature regarding the various barriers that hospital ethnographers encounter as I reflect upon the challenges I faced and the emotional labor I intuitively engaged in and provide advice for researchers on how to navigate these barriers.
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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 | 0.061 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".