Organizational Ethnographic Case Studies: Toward a New Generative In-Depth Qualitative Methodology for Health Care Research?
Bibliographic record
Abstract
A growing body of literature suggests combining organizational ethnography and case study design as a new methodology for investigating complex organizational phenomena in health care contexts. However, the arguments supporting the potential of organizational ethnographic case studies to improve the process and increase the impact of qualitative research in health care is currently underdeveloped. In this article, we aim to explore the methodological potentialities and limitations of combining organizational ethnography and case study to conduct in-depth empirical health care research. We conducted a scoping review, systematically investigating seven bibliographic databases to search, screen, and select empirical articles that employed organizational ethnographic case study to explore organizational phenomena in health care contexts. We screened 573 papers, then completed full-text review of 74 papers identified as relevant based on title and abstract. A total of 18 papers were retained for analysis. Data were extracted and synthesized using a two-phase descriptive and inductive thematic analysis. We then developed a methodological matrix that positions how the impact, contextualization, credibility, and depth of this combined methodology interact to increase the generative power of in-depth qualitative empirical research in health care. Our review reveals that organizational ethnographic case studies have their own distinct methodological identity in the wider domain of qualitative health care research. We argue that by accelerating the research process, enabling various sources of reflexivity, and spreading the depth and contextualization possibilities of empirical investigation of complex organizational phenomena, this combined methodology may stimulate greater academic dynamism and increase the impact of research. Organizational ethnographic case studies appear as a new in-depth qualitative methodology that both challenges and improves the conventional ways we study the lives of organizations and the experiences of actors within the interconnected realms of health care.
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How this classification was reachedexpand
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 | 0.170 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".