Documenting Stigma: A Descriptive Qualitative Study of Psychiatric Emergency Notes of Aggressive Incidents
Bibliographic record
Abstract
Clinical notes, as subjective reconstructions of events, can unintentionally reinforce stigma, perpetuating stereotypes and power imbalances that hinder care and recovery for people receiving care. In psychiatric emergency settings, documentation of aggression incidents may reflect workplace culture, reinforcing perceptions of violence and unpredictability. The aim of this study was to explore the representations of people receiving care conveyed in clinical notes written after incidents of aggression in psychiatric emergencies. A retrospective descriptive qualitative design was used to examine clinical notes reporting aggression incidents from 108 files from a Canadian psychiatric emergency service (2012-2019) collected through the Signature Biobank. Data were analyzed using thematic analysis by Braun and Clark, guided by Link and Phelan's stigma conceptualization theory. Four themes emerged: shaping individual stigmatization through documentation, hierarchical identities revealing a social separation, structural stigmatization, and emergence of a compassionate approach. Findings highlight how institutional changes are needed to ensure more nuanced, reflective, and trauma-informed documentation practices that respect people dignity and experiences. Training in trauma-informed, recovery-oriented, and human rights-based documentation is recommended to reduce stigma and fostering person-centered care. Future research should examine broader institutional practices and explore how training impacts documentation and outcomes for people with mental illness.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".