Combining Critical Ethnography and Critical Discourse Analysis in Mental Health Nursing Research
Bibliographic record
Abstract
BACKGROUND: It is uncommon to combine critical ethnography with critical discourse analysis (CDA) in health research, yet this combination has promise for managing challenges inherent in critical mental health nursing research. OBJECTIVES: This article describes a methodologically innovative way to address issues that arise in the context of critical mental health nursing research. METHODS: This article draws on two studies that each employed a combination of critical ethnography and CDA in the context of mental health nursing research, discussing the challenges and implications of this approach. RESULTS: Although the combination critical ethnography and CDA presents several challenges, it also provides a framework for researchers to sustain a critically reflective stance throughout the research process. This facilitates the process of reanalyzing and reflecting on how healthcare practices and knowledge both support and are constrained by hegemonic discourses. DISCUSSION: This combination has the potential to facilitate the production of new, emancipatory knowledge that will assist nurses in understanding issues of structural inequity within the healthcare system.
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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 | 0.197 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier 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".