Who experiences unmet need for mental health services and what other barriers to accessing health care do they face? Findings from Australia and Canada
Bibliographic record
Abstract
Purpose: To examine factors associated with unmet need for mental health services and links with barriers to access to care more broadly. Methodology: The Commonwealth Fund International Health Policy Surveys from 2013 and 2016 were used to explore factors associated with unmet need for adults who experienced emotional distress for 1320 respondents in Australia and 2284 in Canada. Findings: Over one in five adults in Australia (21%) and in Canada (25%) experienced emotional distress, just over half said they received professional help (51% in Australia, 59% in Canada). The majority of those who did not get help indicated did not want to see a professional (37% in Australia, 30% in Canada). For those who did seek help, the factors associated with not receiving care included lower income, higher out‐of‐pocket health care costs, and poorer health. When compared with people with met needs, those with unmet needs for mental health services were more likely to also experience affordability, medication, and trust‐related access barriers (AOR range 2.41 to 7.49 for the two countries, P < 0.01). Conclusion: Including unmet needs for mental health services as part of regular reporting on access to care may bring attention to access barriers for people with mental health conditions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".