A Scoping Review on Barriers to Mental Healthcare in Canada as Identified by Healthcare Providers
Bibliographic record
Abstract
Aims Mental illness is among the leading causes of disability globally, however the treatment gap is wide even for developed countries. The perspectives of patients and mental healthcare providers are critical to understanding barriers to adequate mental healthcare and developing scalable solutions that improve access and quality of services. However, the views of providers are relatively understudied, precipitating our review to collate and synthesize their perspectives on the barriers to mental healthcare in Canada. Methods We searched MEDLINE/PubMed and PsychINFO for studies with findings in Canada published in English from 2000–2021 with terms for mental health, psychiatry, barriers, and referrals. Included studies were evaluated with the National Institutes of Health Study Quality Assessment Tools and Critical Appraisal Skills Programme. Results 631 papers were screened, finding 20 eligible studies, including 13 qualitative, one cross-sectional, one retrospective, and five mixed-methods studies. Through inductive content analysis, five themes of barriers emerged: (1) patient accessibility (19% of studies), (2) health systems availability and complexity (31%), (3) training/education (25%), (4) work conditions (21%), and (5) cultural sensitivity (4%). Among barriers discussed, common challenges included a lack of resources for both patients and providers, gaps in continuing education for primary care providers, and health systems challenges such as difficulty securing referrals, unclear intake criteria, and confusion due to overload of contacts. Conclusion Health systems face a multi-faceted set of challenges to improving access to mental healthcare that will require solutions from various stakeholders. Understanding these barriers is critical in focusing initiatives to improve mental health care, both in Canada and in countries facing similar challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".