Applying Critical Discourse Analysis to Cross-Cultural Mental Health Recovery Research
Bibliographic record
Abstract
The purpose of this paper is to demonstrate how critical discourse analysis (CDA) frameworks can be used in cross-cultural mental health recovery research. CDA is a qualitative approach that critically appraises how language contributes to producing and reinforcing social inequalities. CDA regards linguistic productions as reflecting, consciously or unconsciously, the narrators' understandings of, or attitudes about, phenomena. Mental health recovery research aims to identify and address power differentials, making CDA a potentially relevant approach. However, CDA frameworks have not been widely applied to mental health recovery research. We adapted established CDA frameworks to our cross-cultural mental health recovery study. The adapted methodology comprises (1) selecting discourses that indicate positive changes and (2) considering sociocultural practices informed by relevant cultural characteristics identified in our previous research, without placing value judgments. Our adapted framework can support cross-cultural mental health recovery research that uses CDA.
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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 | 0.182 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".