Guidance on Performing Focused Ethnographies with an Emphasis on Healthcare Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Focused ethnographies can have meaningful and useful application in primary care, community, or hospital healthcare practice, and are often used to determine ways to improve care and care processes. They can be pragmatic and efficient ways to capture data on a specific topic of importance to individual clinicians or clinical specialties. While many examples of focused ethnographies are available in the literature, there is a limited availability of guidance documents for conducting this research. This paper defines focused ethnographies, locates them within the ethnographic genre, justifies their use in healthcare research, and outlines the methodological processes including those related to sampling, data collection and maintaining rigour. It also identifies and provides a summary of some recent focused ethnographies conducted in healthcare research. While the emphasis is placed on healthcare research, focused ethnographies can be applicable to any discipline whenever there is a desire to explore specific cultural perspectives held by sub - groups of people within a context - specific and problem - focused framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.146 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it