Bibliographic record
Abstract
This article is simultaneously a personal narrative and a Foucauldian analysis of the author's embodied reality as a racialized woman hospitalized in a Western psychiatric facility. The author is the narrator, the mental patient (main character of the story) and the person who deconstructs the experience (researcher, insider). This unique position – a South-Asian patient and a researcher/scholar trained in a Western academic institute – provides her an opportunity to give voice to her direct experiences in the psychiatric facility, as well as describe her reality of mental illness. Such rare, self-reflective ‘insider’ perspectives challenge the Euro-centric conceptualizations of ‘mental health’ and may be unsettling to the psychiatry/psychotherapy and/or academic communities. As our population becomes more diverse, mental health professionals and policy makers will need this knowledge to broaden their understanding of how members of diverse cultures may experience mental health, as well as to support them in their cultural ways of self-healing. Social science research methodologies that do not further reinforce the binary – us/them, expert/client – offer a holistic understanding of clients' mental health and are necessary to develop effective psychotherapeutic interventions. Key words: mental health, psychiatry, immigrant women, culture, ethnography, Foucault.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.028 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".