Applying ethnography to the study of context in healthcare quality and safety
Bibliographic record
Abstract
BACKGROUND: Translating and scaling healthcare quality improvement (QI) and patient safety interventions remains a significant challenge. Context has been identified as a major factor in this. QI and patient safety research have begun to focus on context, with ethnography seen as a promising methodology for understanding the professional, organisational and cultural aspects of context. While ethnography is used to investigate the context of a variety of QI and safety interventions, the challenges inherent in effectively importing a qualitative methodology and its social science practitioners into this work have been largely unexamined. METHOD AND RESULTS: We explain ethnography as a research practice grounded in theory and dependent on observations gathered and interpreted in particular ways. We then review the approach of health services literature to evaluating this sort of qualitative research. Although the study of context is an interest shared by both social scientists and healthcare QI and safety researchers, we identify three key points at which those 'exporting' ethnography as a methodology and those 'importing' it to deal with QI and safety challenges may diverge. We describe perspectival divergences on the methodology's mission, form and scale. At the level of mission we demonstrate how ethnography has been adapted to a 'describe and feed back' role in the service of QI. At the level of form, we show how the long-term embedded observation at the heart of ethnography can be adapted only so far to accommodate QI interests if both data quality and ethical standards are to be upheld. Finally, at the level of scale, we demonstrate one ethnographic study design that balances breadth of exposure with depth of experience in its observations and so generates a particular type of scalable findings. SUMMARY: The effective export of ethnography into QI and safety research requires discussion and negotiation between social scientific and health services research perspectives, as well as creative approaches to producing self-reflexive data that will allow clinicians to understand their own context and so improve their own processes.
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.
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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".