Predictors of barriers to accessing youth mental health and/or addiction care.
Bibliographic record
Abstract
Background: It is estimated that 1.2 million Canadian youth are affected by mental health and/or addiction issues; yet only a small proportion of young people receive appropriate and specialized treatment. Given caregivers are often tasked with navigating the complex mental health and/or addiction care systems for their youth, it is important to identify and understand the characteristics, such as those related to youth and their families, that are associated with caregivers' perceived barriers to accessing youth mental health and/or addiction services. Objective: The objective of this cross-sectional survey study was to examine the unique predictors of caregivers' perceived barriers to accessing youth mental health and/or addiction services. Method: = 5.3) with mental health and/or addiction issues in Ontario, Canada identified from a community-based online survey. Results: Regression results showed that caregivers' demographics (i.e., living in a rural area, having an education level of college/university degree or higher), youth having concurrent issues, and service use patterns (i.e., currently accessing and/or seeking services) significantly predicted a higher level of barriers to accessing mental health and/or addiction services. Conclusion: In order to improve access to care for youth with mental health and/or addiction issues, understanding the predictors of barriers to accessing appropriate services is an important step in making services more accessible for youth and families.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".