Positive mental health among children 11 years and under in Western countries: a scoping review to inform Canada’s public health surveillance
Bibliographic record
Abstract
Abstract Background The Public Health Agency of Canada developed the Positive Mental Health Surveillance Indicator Framework (PMHSIF), which is used to monitor positive mental health (PMH) and its determinants in Canada. While adult and youth versions of the PMHSIF were released, additional research is needed to identify relevant and age-appropriate concepts of PMH among children. Methods A scoping review of peer-reviewed and grey literature was conducted to examine how PMH is conceptualized and measured among children (< 12 years). Online academic databases were searched up until January 31, 2023. Grey literature sources were conducted up until October 3, 2023. Data on study characteristics were extracted and some were tallied and explained. Measures of PMH were categorized as self- or other-rated. Results A total of 636 documents were identified through the search strategies. Of these, 65 documents from the grey literature and 39 peer-reviewed papers were included in this review. Many of the articles (74%) mentioned at least one theory in the introduction/background. The PMH concepts that emerged included: hedonic well-being (n=77), psychological well-being (n=41), social well-being (n=30), and social emotional learning and/or positive development (n=19). Various risk and protective factors were extracted. Conclusion The findings support the use of the existing Canadian PMH conceptual framework for children, albeit with small modifications to ensure that child-relevant pieces are reflected. Additional work is needed before these results can be used for national surveillance. This review addresses a gap in the literature, encourages routine reporting of child PMH across Canada, and better informs public health policy.
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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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.035 | 0.041 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".