Opening Minds: The Mental Health Commission of Canada’s Anti-Stigma Initiative: Preface
Bibliographic record
Abstract
Fear of being stigmatized or labelled is a major reason that many people living with a mental illness will not seek help. They report stigma is more life-limiting and disabling than the illness itself. Stigma can mean being excluded by friends and family, being discriminated against by health care professionals, and being treated unfairly in the workplace. In 2001, the World Health Organization declared stigma as “the single most important barrier to overcome.”1, p 98 As part of its 10-year mandate, the Mental Health Commission of Canada embarked on an anti-stigma initiative called Opening Minds (OM) to change the attitudes and behaviours of Canadians toward people with a mental illness and to encourage people and organizations to eliminate discrimination. Launched in 2009, OM is the largest systematic effort undertaken in Canadian history to reduce the stigma and discrimination associated with mental illness. OM has taken a targeted and evidence-based approach, initially reaching out to health care providers, youth, the workforce, and news media. OM’s philosophy is not to reinvent the wheel, but rather to build on the strengths of existing programs from across the county. As a result, OM has actively sought out such programs, few of which have been scientifically evaluated for their effectiveness. Now partnering with more than 100 organizations, OM has conducted evaluations of the programs to determine their effectiveness at reducing stigma. OM’s goal is to replicate successful programs nationally. A key component of programs being evaluated is contact-based educational sessions, where target audiences hear personal stories from and interact with people who have recovered or are successfully managing their mental illness. The success of contact-based anti-stigma interventions has been generally supported in international studies as a promising practice to reduce stigma. OM’s numerous partner programs, sponsoring organizations, and research teams provide a powerful and growing network across Canada that is reaching out nationally to make a real difference in the effort to reduce stigma. Up to this point, most groups attempting to tackle stigma have principally used social marketing campaigns; however, this approach has not proven to be effective in the long term. Instead, OM has chosen a different strategy, one that has shown considerable success in breaking down the stigma barrier across the target groups identified. This supplement is intended to provide an overview of the activities that have been conducted by the OM research teams. It includes a study2 that identifies the key ingredients of successful anti-stigma interventions for health care providers. It is based on the evaluation of more than 20 programs directed at this professional group. Also included is a paper3 that presents results of the first population survey of stigma in Canada. This study found youth aged 12 to 25 reported the highest level of personal stigma. The final paper4 in this supplement explores the factors that could serve as the basic requirements for an anti-stigma program to produce sufficient savings to pay for itself.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".