Mental Health Services in Canada: Building a Model of Mental Health Care Utilization
Bibliographic record
Abstract
Existing research literature shows that mental health care services are under-utilized among individuals who have mental health problems. In Canada, it is estimated that only 40% of individuals who have mental health problems are provided with mental health care. Although some past research have examined predictors of mental health care utilization, there are gaps in our knowledge of how these predictors interact with one another and how these predictors specifically affect mental health care utilization in Canada. In this study, data from the 2007/2008 Canadian Community Health Survey (CCHS) were used with the goal of developing a more accurate picture of trends in mental health issues and care in Canada (N = 131,061, weighted N = 28,030,943). Guided by Andersen’s Behavioural Model of Health Care Use (2008), associations were examined between mental health care utilization (i.e., consultation with a psychologist, accessing mental health care services, and receiving care from a mental health specialist) and contextual factors (e.g., province of residence, health region), predisposing individual characteristics (e.g., gender, age, minority status), individual enabling factors (e.g., employment, income), individual need (i.e., stress, mental health well-being), general health behaviours (e.g., number of consults with health professional), and outcomes factors (e.g., satisfaction, difficulties getting services) were explored. Associations were observed between mental health care utilization and a variety of variables across most of the categories proposed by Andersen (2008). When tested as a model, the group of variables related to need generally showed the strongest influence on utilization. However, when examining individual variables contributing to the model, four predictors (perceived mental health, physician visits, income, and age) generally made the largest contributions to the models. These predictors represented factors proposed in Andersen’s model. The models were statistically significant, however, both models were limited in the amount of variation explained for consultation with a psychologist and receiving mental health care (Nagelkerke R2 = .145 and Nagelkerke R2 = .271 respectively). Clinical and theoretical implications and future research directions are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".