Combining Critical Ethnography and Critical Discourse Analysis in Mental Health Nursing 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
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.
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 | 0.101 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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