Perceived Barriers to Mental Health Service Utilization in the United States, Ontario, and the Netherlands
Bibliographic record
Abstract
OBJECTIVE: Although rates of mental health service utilization differ dramatically across countries, little information is available about differences in self-reported barriers to mental health service utilization. Perceived barriers were examined in three locations with differing health care systems. METHODS: Data came from three methodologically similar population-based surveys of adults conducted in the 1990s in Ontario, Canada (N=6,261), the United States (N=5,384), and the Netherlands (N=6,031) that assessed DSM-III-R nonpsychotic mental disorders with the Composite International Diagnostic Interview. Respondents who reported a need for professional help were asked to indicate reasons for not seeking care. Multiple logistic regression analyses were used to determine the sociodemographic, mental disorder, and location-specific correlates of each perceived barrier. RESULTS: The pattern of reported barriers to mental health service utilization was similar across locations: attitudinal barriers (thoughts that the problem would get better on its own) were more prevalent than structural barriers (inability to get an appointment). Fear of stigmatization was not commonly endorsed. With adjustment for sociodemographic factors and type of mental disorder, low-income respondents were significantly more likely to report a financial barrier in the United States than in either Ontario or the Netherlands. CONCLUSIONS: Across locations, attitudinal barriers were more likely to be endorsed than structural barriers to service utilization. The most striking reported cross-national difference was structural, with many more U.S. respondents (especially those with low incomes) reporting financial barriers than respondents in either Ontario or the Netherlands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".