Opening Minds: The Mental Health Commission of Canada’s Anti-Stigma Initiative: Key Ingredients of Anti-Stigma Programs for Health Care Providers: A Data Synthesis of Evaluative Studies
Bibliographic record
Abstract
As part of its OM Anti-Stigma Initiative, the MHCC partnered with organizations and investigators conducting anti-stigma interventions targeting various health care provider groups in Canada, with the purpose of evaluating program outcomes.1 Using existing evidence on the value of social contact2–5 as an initial point of departure, OM partnered with programs using some form of social contact or contact-based education in the delivery of their program. Typically, social contact-based approaches emphasize the inclusion of planned exchanges between people with lived experience of mental illness and the target audience as a part of the program curriculum.5 In many cases, target audiences hear personal stories from, and (or) interact with, people who have recovered or are successfully managing a mental illness. While all programs evaluated by OM included some form of social contact, the extent and nature of the contact varied from program to program, as did many other characteristics, including program length, educational emphasis, program context and delivery features, and target audience (for example, practicing professionals, compared with students). Online eTable 1 contains a description of the various partner programs, their targeted audiences, and their main program elements. To enhance the comparability of the various studies, OM developed and adopted a common outcome scale, the OMS-HC,6,7 and had data-sharing arrangements with its partners. Two RCTs were first conducted to confirm the general effectiveness of the contact-based approach. Both trials returned positive results.8,9 Subsequently, with efficacy confirmed, the goal became the identification of characteristics associated with maximal effectiveness. Most of the evaluative studies used a before-and-after study comparison to evaluate effectiveness, and data collected in this way became the main source of data for assessing program characteristics or key ingredients associated with the best outcomes. With 22 total pre–post data sets from a diverse set of studies (but all using the OMS-HC), it became necessary to identify a systematic approach to quantifying the outcomes associated with each potential key ingredient. Analysis of individual study results had not identified individual characteristics (such as age, sex, or whether a person had a friend or close relative with a mental illness) as being significant determinants of outcome.1,8,9 For this reason, we chose a strategy based on contrasting study-level characteristics using methods commonly employed in meta-analysis, including meta-regression. These techniques can accommodate heterogeneity across studies and provide a method of weighting the contributions of larger and smaller studies when generating pooled effect estimates. Implementation of the overall strategy required a multi-phased, mixed-methods approach. First, a qualitative study was required to identify potentially important program characteristics and to accurately classify each intervention according to those characteristics. Next, the aforementioned quantitative strategies were used to evaluate the impact of these characteristics on outcomes. In our paper, we report the comparative evaluation of anti-stigma interventions affiliated with OM, including the elements of these programs found to be associated with the most favourable outcomes. Clinical Implications Anti-stigma interventions incorporating social contact are effective in a broad range of health care providers and trainees. Programs that include a recovery emphasis, personal testimony from a trained speaker who has lived experience of mental illness, that employ multiple forms of social contact, that teach skills involving what to say and what to do, that employ myth-busting, and that use an enthusiastic facilitator perform significantly better than programs that include only some of these ingredients. A recovery emphasis and having multiple forms of social contact are especially critical for maximizing outcomes. Limitations The studies evaluated here consisted of before-and-after comparisons and were usually uncontrolled. Considerable heterogeneity was observed even after modelling for intervention ingredients. Other important determinants of outcomes remain to be identified. Most of these evaluations were short-term, leaving unanswered questions about the long-term effects of anti-stigma interventions.
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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 | 0.087 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".