Family Perspectives on Pathways to Mental Health Care for Children and Youth in Rural Communities
Bibliographic record
Abstract
CONTEXT: There is insufficient literature documenting the mental health experiences and needs of rural communities, and a lack of focus on children in particular. This is of concern given that up to 20% of children and youth suffer from a diagnosable mental health problem. PURPOSE: This study examines issues of access to mental health care for children and youth in rural communities from the family perspective. METHODS: In-depth interviews were conducted in rural Ontario, Canada, with 30 parents of children aged 3-17 who had been diagnosed with emotional and behavioral disorders. FINDINGS: Interview data indicate 3 overall thematic areas that describe the main barriers and facilitators to care. These include personal, systemic, and environmental factors. Family members are constantly negotiating ongoing tension, struggle, and contradiction vis-à-vis their attempts to access and provide mental health care. Most factors identified as barriers are also, under different circumstances, facilitators. Analysis clustered around the contrasts, contradictions, and paradoxes present throughout the interviews. CONCLUSIONS: The route to mental health care for children in rural communities is complex, dynamic, and nonlinear, with multiple roadblocks. Although faced with multiple roadblocks, there are also several factors that help minimize these barriers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".