Labelling of mental illness in a paediatric emergency department and its implications for stigma reduction education
Bibliographic record
Abstract
INTRODUCTION: Stigmatizing attitudes and behaviours towards patients with mental illness have negative consequences on their health. Despite research regarding educational and social contact-based interventions to reduce stigma, there are limitations to the success of these interventions for individuals with deeply held stigmatizing beliefs. Our study sought to better understand the process of implicit mental illness stigma in the setting of a paediatric emergency department to inform the design of future educational interventions. METHODS: We conducted a qualitative exploration of mental illness stigma with interviews including physician, nurse, service user, caregiver and administrative staff participants (n = 24). We utilized the implicit association test as a discussion prompt to explore stigma outside of conscious awareness. We conducted our study utilizing constructivist grounded theory methodology, including purposeful theoretical sampling and constant comparative analysis. RESULTS: Our study found that the confluence of socio-cultural, cognitive and emotional forces results in labelling of patients with mental illness as time-consuming, unpredictable and/or unfixable. These labels lead to unintentional avoidance behaviours from staff which are perceived as prejudicial and discriminatory by patients and caregivers. Participants emphasized education as the most useful intervention to reduce stigma, suggesting that educational interventions should focus on patient-provider relationships to foster humanizing labels for individuals with mental illness and by promoting provider empathy and engagement. DISCUSSION: Our results suggest that educational interventions that target negative attributions, consider socio-cultural contexts and facilitate positive emotions in healthcare providers may be useful. Our findings may inform further research and interventions to reduce stereotypes towards marginalized groups in healthcare settings.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".