Understanding Stigma: A Pooled Analysis of a National Program Aimed at Health Care Providers to Reduce Stigma towards Patients with a Mental Illness
Bibliographic record
Abstract
Background and Objectives The problem of mental illness-related stigma within healthcare is an area of increasing attention and concern. Understanding Stigma is an anti-stigma workshop for healthcare providers that uses social contact as a core teaching element, along with educational and action-oriented components. The objective of our study was to determine the impact of this program on healthcare providers’ attitudes and behavioural intentions towards patients with a mental illness, and also to ascertain whether various participant and program characteristics affected program outcomes. Our paper reports the results of a pooled analysis from multiple replications of this program in different Canadian jurisdictions between 2013 and 2015. Material and Methods We undertook a pooled analysis of six separate replications of the Understanding Stigma program. All program replications were evaluated using a non-randomized quasi experimental pre- post- follow-up design. The Opening Minds Scale for Health Providers (OMS-HC) was used as the main assessment tool. Study-level and individual-level meta-analysis methods were used to synthesize the data. First, the ‘metan’ command was used to show outcomes by study, using a forest plot. Then, a pooled dataset was produced and analyzed using a random intercept linear mixed model approach with each program being modelled as a random effect. Program and participant characteristics were examined as independent variables using this approach. These were each entered individually. Individual tests included pre to post change by program version (original or condensed), by occupation (nurses versus other healthcare providers), by gender, age, and previous diagnosis of a mental illness. Results Program effect sizes ranged from .19 to .51 (Cohen’s d), with an overall combined effect size of .30. The results of the mixed model analysis showed the improvement from pre to post intervention was statistically significant for the total scale and subscales. Analysis of program and participant factors found that version type, healthcare provider type, gender, and previous diagnosis of a mental illness were all non-significant factors on program outcomes. A significant inverse association was revealed between increasing age and score change. Results also showed a significant positive linear relationship between baseline score and improvement from pre to post intervention. Maintenance of scores at follow-up was observed for participants who attended a booster session. Conclusions The results are promising for the effectiveness of this brief intervention model for reducing stigmatizing attitudes and improving behavioural intentions among nurses and other healthcare providers.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".