Guidance on Performing Focused Ethnographies with an Emphasis on Healthcare Research
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.
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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 | 0.132 | 0.268 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.060 |
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".