MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
→
Sort
Language
Type
Field
Venue
Topic
Suicide and Self-Harm Studies
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,664 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,664 works in the cohort · of 4,299,418page 45 of 74

Labels cover 9 of 3,664 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,664 of 3,664 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Further Silencing the Voiceless
Penelope Hasking, Stephen P. Lewis, Lexy Staniland, Sylvanna Mirichlis, Kirsty Hird, Nicole Gray +4 more
2023· article· en· The Journal of Nervous and Mental Disease· Psychology
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Helping people who self-harm
Helen E. Blackwell, Lucy Palmer, Graham Hinchcliffe
2008· article· en· Emergency Nurse· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
affvenueaboutunlabeled
A new suicide risk assessment tool in Nova Scotia, Canada
Joseph Sadek, Mary Pyche, Scott Theriault, Nicholas J. Delva, Sonia Chehil, David Pilon
2020· article· en· Clinical and investigative medicine· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Self-Immolation in High-Income Countries
Renato Antunes dos Santos, Bipin Ravindran, Saulo Castel, Eduardo Chachamovich
2021· book-chapter· en· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
venueno affno abstractunlabeled
Suicide in Adolescents in Italy (1969–1994)
Iginia Mancinelli, Lorella Ceciarelli, Anna Comparelli, Simone Lazanio, Paolo Girardi, Roberto Tatarelli
2001· letter· en· The Canadian Journal of Psychiatry· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Prediction of Suicide Risk Using Machine Learning and Big Data
Thiago Henrique Roza, Thyago Antonelli Salgado, Cristiane Santos Machado, Devon Watts, Júlio Bebber, Thales Renato Ochotorena de Freitas +3 more
2023· book-chapter· en· Psychology
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About