Stigma of addiction and mental illness in healthcare: The case of patients’ experiences in dental settings
Bibliographic record
Abstract
OBJECTIVE: To explore the ways in which stigma is experienced in healthcare and dental settings by patients with a history of addiction and mental illness. METHODS: Audio-recorded, semi-structured interviews with a purposefully selected convenience sample of residents from two community treatment centres in Vancouver, Canada were conducted. The interview guide contained questions about experiences while seeking health and dental care and was based on an existing framework of labeling, stereotyping, exclusion, discrimination, and power imbalance. Interviews were transcribed verbatim for coding and thematic analysis. RESULTS: Twenty-five participants between 23 and 67 years of age were interviewed; 17 were males. Most had a self-reported history of depression combined with use of alcohol and crack-cocaine; most of them only sought dental care for emergency purposes. Textual analysis of more than 300 pages of transcribed interviews revealed that participants perceived stigma when they were negatively stereotyped as 'unworthy', labeled as 'different', excluded from the decision-making process, discriminated against, 'treated unfairly', and felt powerless when interacting in the heath and dental care systems. Conversely, positive experiences were characterized by empathy, reassurance and good communication, which were empowering for patients. CONCLUSIONS: When associated with stigma, mental illness and addictions have negative implications for accessing health and dental care. From our participants' perspectives, it seems that the lack of understanding about their life conditions by the healthcare professionals was the origin of stigma. We suggest that an increased social awareness of these health issues be enhanced among current and future health and dental care professionals to help improve care experiences for this marginalized population.
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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 | 0.006 | 0.012 |
| 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 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".