Mental Health Walk-In Clinics for Children and Families
Bibliographic record
Abstract
Approximately one in five children in Ontario have a mental health disorder causing significant distress and/or impairment. Yet only a third of these children receive specialized mental health services. One of the barriers to accessing care is long waitlists. Mental health walk-in clinics (MHWCs) can help address this issue by providing immediate support and removing administrative hassles for families (e.g., phone calls, intakes). The current dissertation sought to better understand the availability, implementation, and use of MHWCs.\nStudy 1 explored the availability of MHWCs across Canada. A brief survey was distributed to child and youth mental health (CYMH) agencies. Many CYMH agencies (73% total sample; 69% random sample) reported using this service delivery model. Study 2 examined the implementation of MHWCs in Ontario. An in-depth survey was distributed to CYMH agencies in Ontario. MHWCs are being used to provide timely and accessible services, as well as to serve as a point of intake for 44% of agencies. MHWCs are provided in different locations (e.g., agencies, schools) using different modalities (e.g., consulting break) and approaches (e.g., solution focused therapy). Most agencies quickly adapted to COVID-19 restrictions by providing virtual MHWCs.\nStudies 3 and 4 explored how families use MHWCs. Administrative data from two CYMH agencies in Ontario capturing a period of 3 to 6 years were extracted. Overall, 24% and 61% of families in Agency 1 and 2, respectively, used MHWCs at least once. About a third of families using MHWCs had 2 or more visits. Younger children, children in shared custody or under the guardianship of their birth/adoptive mother or father, and children that were not referred to other agency services had higher risk of a second MHWC visit. Over half of families use MHWCs alongside other agency services; most often, earlier in the service use trajectory. Older children, children in shared custody, and children who were referred to other agency services had higher odds of MHWC use before other services.\nIn summary, MHWCs were a common service delivery model in the CYMH agencies sampled in Ontario and in Canada. It is flexible, such that the implementation (e.g., modality, approach) can be tailored to fit the needs of an agency and community. MHWCs may be sufficient for 33-43% of families. Providing help in a MHWC visit without requiring a comprehensive intake or asking families to wait should enhance access for other families who require more intensive services. For other families (57-67%), MHWCs can help support them at the beginning of their service journey by providing initial support and linking them to other agency services.
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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".