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Record W4411454141 · doi:10.61959/monj4379e

Facts and Stats: Families and Mental Health in Canada

2017· report· en· W4411454141 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMental illnessMental health careFamily memberPsychologyPsychiatryMental health lawMedicineFamily medicine

Abstract

fetched live from OpenAlex

At some point, most families find themselves affected by mental illness, whether it’s because a family member (or multiple family members) personally experiences a mental health condition or because they’re providing care to someone else – or both. With appropriate treatment and support, however, most people who experience a mental illness will recover,1 and families often play an important role in providing, arranging or helping pay for care. Our new fact sheet provides an overview of families and mental health in Canada, including rates of mental health conditions within families, factors that contribute to mental illnesses and the roles family members can play in mental health treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.011
Science and technology studies0.0080.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0290.003

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.

Opus teacher head0.041
GPT teacher head0.351
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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