Assessing Public Demand for Mental Health Insurance: The Role of Self-Interest and Ideology
Bibliographic record
Abstract
Despite the spike in mental health disorders since the disruptions caused by the COVID-19 pandemic, most OECD countries are still lacking a comprehensive public coverage of mental health care. This situation is puzzling considering that a public insurance is not particularly fiscally costly. Indeed, cost-benefit analyses have shown that a public insurance would increase access to treatments, which would, in turn, generate economic gains by improving the productivity of the population. In this article, we study if a lack of public demand represents one of the reasons why we are not seeing a proliferation of public mental health insurance programs. Mental health researchers have spent very little time thinking about political economy, while political economists do not study mental health policies. Most mental health researchers assume that stigma (culture) is the cause of weak mental health provision. The few studies on the issue have shown that stigma against mental health disorders is associated with weaker support for public spending on mental health care. Yet mental health problem occurrence and stigma don’t vary so much and can’t explain cross country variation. Hence, mental health specialist cannot explain why supply varies between countries. Contributions: • We are applying a political science framework to study mental health policy preferences. • Most previous studies do not analyze the interplay of self-interest and ideology to explain individuals’ willingness to pay for the expansion of public coverage for mental health care. We highlight the role of ideological factors in explaining preference for public mental health care and by mapping the coalitions for and against the extension of public insurance. • There is also a lack of research assessing the influence of individuals' personal experience with mental health, whether they use treatments themselves, or whether they themselves are covered by private insurance and support for greater public insurance coverage. Moreover, there is a paucity of public opinion studies on the issue in Canada. • The Canadian context is pertinent because the public coverage health care is traditionally limited to care provided by doctors and hospitals and doesn’t include several non-medical health services, such as psychotherapy. In this article, we rely on an original survey conducted by the firm Léger with a representative sample of the Quebec population (N=1000), a province in which one major party proposed a public insurance for psychological services at the last provincial election in 2022.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".