POSITIVE MENTAL HEALTH SURVEILLANCE INDICATOR FRAMEWORK\nQUICK STATS, YOUTH (12 TO 17 YEARS OF AGE), CANADA, 2017 EDITION
Bibliographic record
Abstract
Positive mental health is a state of well-being that allows people to feel, think and act in ways that enhance the ability to enjoy life\nand deal with challenges.1 The Positive Mental Health Surveillance Indicator Framework (âthe Frameworkâ) provides comprehensive,\nhigh quality information on the outcomes and risk and protective factors associated with positive mental health across four domains\n(individual, family, community and society), to support research and policy development. The release of the Framework for youth\naged 12 to 17 years is the second in a series; the Framework for adults aged 18 years and older was released in early 2016.2 The Framework\nwas developed in consultation with stakeholders working in mental health surveillance, programs and policy. The details of the\ndevelopment of the Frameworks across the life course, for adults, youth and children, can be found in the paper âMonitoring positive\nmental health and its determinants.â3 More data on positive mental health can be found online using the Public Health Agency of\nCanadaâs interactive data tool, âInfobase.â4
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".