Child and Adolescent Mental Health Service Management Strategies that may Influence Wait Times.
Bibliographic record
Abstract
OBJECTIVES: (1) To describe the strategies employed by child mental health agencies to manage service demands; (2) to determine whether the types of strategies used are related to meeting Canadian Psychiatric Association (CPA) benchmarks and wait times; and, (3) to determine whether the types of strategies used are related to agency characteristics. METHODS: An online questionnaire was distributed to 379 agencies providing child mental health services in Canada. The survey inquired about agency characteristics, wait times, ability to meet benchmarks and a series of strategies which may impact wait times. Spearman's rank correlations were used to determine relationships between variables. RESULTS: One hundred thirteen agencies returned adequately completed surveys (29.8%). Collaborating with other agencies/providers and referring families to self-help resources were the most commonly endorsed strategies. The use of more upstream/ pre-waitlist strategies was related to the ability to meet CPA benchmarks for urgent cases. No cluster of strategies was related to estimated wait times. Restriction strategies were most consistently related to agency size. CONCLUSIONS: Multiple strategies were endorsed by many agencies, but very few demonstrated relationships to wait time variables. Rigorous evaluation of commonly used service strategies are required to determine whether any positive impacts are being obtained by such efforts.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".