My Week of Denial and Disorientation: A Lived Experience Narrative of a Stay in a Psychiatric Emergency Department
Bibliographic record
Abstract
BACKGROUND: Psychiatric emergency departments (EDs) are common settings in which patients receive crisis care, yet their experiences in these environments remain understudied. AIM: This lived experience narrative recounts the first author's week-long stay in a psychiatric ED, providing insight into the experiences and challenges of inpatient psychiatric care. METHODS: The first author used a narrative approach to develop a series of vignettes that captured significant moments of her inpatient experience. Both authors reflected on these experiences, drawing on professional expertise and existing literature. FINDINGS: The narrative reveals a lack of communication on the unit, power imbalances between patients and staff and the dismissal of patients' concerns, experiences and identities. It illustrates how patients' behaviours are often misinterpreted, contributing to further distress and disempowerment. DISCUSSION: The authors examine systemic problems in mental health care, such as epistemic injustice, the dominance of the biomedical model and restrictive control over patient autonomy. They argue for the need to shift to a more compassionate, pluralistic and trauma-informed approach to mental health care. CONCLUSION: This narrative highlights the need for reforms in emergency psychiatric care. By centring patients' voices, mental health services can foster a more respectful and healing environment for people in crisis.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".