The Encounters and Challenges of Ethnography as a Methodology in Health Research
Bibliographic record
Abstract
Medical anthropology has existed since the early 1960s, and the encounters of ethnography in health research are recent. We will trace key historical markers and highlight several ethnographic studies in health research in this article. In particular, we are interested how aspects of classic ethnographic work have been taken up, and how the use has changed over time, as ethnographies, such as focused ethnographies and other forms of ethnography, have developed in health research. Understandings of culture have shifted and led to redefinitions of culture, and some key elements of ethnographic research have been lost. Ethnographies conducted in health research often do not focus on culture from a broader perspective; instead, the focus is on single health-related issues. Health researchers appear to spend less time in the field, time spent in the field is regarded as less important, and the importance of the context of field notes is underestimated.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.596 | 0.474 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.022 | 0.188 |
| Scholarly communication | 0.034 | 0.050 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".