Mental health and addiction concerns in northern Ontario: A cross-sectional study of sociodemographic predictors, access barriers, and service needs
Bibliographic record
Abstract
Access to mental health and/or addiction (MHA) services is limited in rural and remote regions, especially in geographically diverse areas such as Ontario, Canada. Moreover, available services may not be able to address the unique needs of those seeking support. To effectively address local MHA service needs, it is necessary to understand predictors of MHA concerns and the experiences of those accessing care in rural areas such as northern Ontario. The current study focused on individuals living in northern Ontario and aimed to 1. Identify sociodemographic factors that predict their MHA concerns; 2. Identify common barriers experienced by those seeking MHA services; and 3. Explore their MHA support needs. Survey data were collected online from 500 northern Ontario residents (aged 18+) between January and March 2022. Univariate statistics were used to describe MHA service access, barriers, and needs, and adjusted multiple logistic regression was conducted to assess predictors of MHA concerns. Younger age (Odds Ratio (OR) = 0.972), low socioeconomic status (middle: OR = 0.491; high: OR = 0.436), identifying as non-straight/non-heterosexual (straight/heterosexual: OR = 0.336), identifying as married (unmarried: OR = 0.507), and dissatisfaction with social support (OR = 6.410) were significant predictors of MHA concerns. Although most (76.8%) of the sample reported MHA concerns, less than a quarter of the sample accessed support. Most frequently accessed services tended to be less specialized, and most frequently reported access barriers were mainly systemic. The current study describes predictors of MHA concern as well as the unique MHA-service-related experiences and needs of northern Ontario residents. These findings may be considered in efforts to develop MHA tools and supports that align with local needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".