Government Monitoring of the Mental Health of Children in Canada: Five Surveys (Part II).
Bibliographic record
Abstract
OBJECTIVE: Canadian governments spend billions of dollars yearly on programmatic interventions, intended to improve the mental health of children, without recommended monitoring of children's mental health. The Canadian Academy of Child and Adolescent Psychiatry monitored governments' progress in producing reports. METHOD: Five evolving surveys were done during 2002, 2004, 2005, 2006 and 2008. Initially, progress was monitored then later surveys examined challenges that inhibited monitoring, the need for a national strategy, an indicator framework and an agency to do the monitoring and the role of non-government organizations. The 2008 survey requested the three most important indicators governments desired, and created clarity in the definition of monitoring reports in contents, criteria, qualities of indicators and potential names. For comparison purposes, a Partnership Model to survey populations was evaluated. RESULTS: Over five surveys, 13 of 14 governments affirmed the desire for monitoring and 64 publications were reviewed and categorized. No reports met criteria for 'monitoring reports'. The Partnership Model was used successfully in 11 Provincial-Territorial governments. CONCLUSIONS: It was reassuring that governments supported monitoring and were producing reports. The Partnership Model may offer a suitable alternative for governments. Results of 2006 and 2008, discussion, conclusions and references are in Part II.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".