Behind the Stigma: A Narrative Inquiry into the Perception and Experiences of Mental Health, Addictions, and Forensic Nurses
Bibliographic record
Abstract
Mental health and forensic nurses work at the intersection of healthcare, law, and social justice, yet their roles remain undervalued within the nursing profession. This study explored how these nurses construct professional identity and resilience while navigating stigma and systemic inequity. A qualitative narrative inquiry design was used to gather written reflections from 14 nurses in Ontario and British Columbia, including registered nurses, registered practical nurses, and nurse practitioners. Participants responded to open-ended prompts through a secure online platform, describing experiences of stigma, workplace hostility, advocacy, and meaning in their work. Data were analyzed thematically and through a composite narrative approach to capture both individual and collective perspectives. Six major themes emerged: stigma and systemic misrepresentation, stigma toward patients, advocacy and emotional labor, workplace hostility, purpose and resilience, and systemic barriers. Findings demonstrate that nurses experience both external and internalized stigma that diminishes professional legitimacy, yet they construct identities grounded in empathy, advocacy, and relational expertise. The study applies Goffman’s concept of courtesy stigma and social identity theory to interpret how hierarchies shape belonging within healthcare. These results stress the need for stigma-reduction education, mentorship programs, and policy investment in community mental health services. Centering nurses’ voices through narrative inquiry reframes mental health and forensic nursing as advanced, relational, and justice-oriented practice.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".