Bibliographic record
Abstract
T his second instalment in our Child Health in Canada series explores a multi-faceted topic that weighs especially heavy on the minds of parents, teachers, care providers, policy makers, social workers and many others: mental health.After all, as Stan Kutcher asserts in his contribution to this issue, "there can be no health without mental health."The mental well-being of our children and youth is a major cause for concern.In Ontario, for instance, half a million children grapple with mental health problems (Children's Mental Health Ontario [CMHO] 2010a).A recent study in the United States similarly revealed that approximately one in five young people in that country -the same proportion as in Ontario (CMHO 2010a) -suffer from a "mental disorder" that is severe enough to undermine their normal functioning (National Institute of Mental Health 2010, September 27).The consequences of leaving such problems untreated include school failure, family conflict, drug abuse, violence and suicide (CMHO 2010b).And we should never forget that mental health problems among the young are not neatly confined to the early years: 70% of Canadian adults who have mental health issues developed symptoms before age 18 (Mental Health Commission of Canada [MHCC] 2010).
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".