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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.

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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.

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

3,522 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,522 works in the cohort · of 4,299,418page 67 of 71

Labels cover 18 of 3,522 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 3,522 of 3,522 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.

aboutno affunlabeled
Open Access
2014· article· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Notes on setting up residency electives
Sandra Stevenson
2000· article· en· Paediatrics & Child Health· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Alun Lloyd Thomas
H. Thomas
2002· article· es· BMJ· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueaboutno affunlabeled
70. Supporting IMG integration into residency trainings
Susan Glover Takahashi, Mitchell Alameddine, Dawn Martin, Sarita Verma, Sarah Edwards
2007· article· en· Clinical and investigative medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Indigenous Peoples Living with Multiple Sclerosis in Canada
Nabeela Nathoo, Rheanna Robinson, Scott E. Jarvis, Erin F. Balcom, Janice Y. Kung, Penelope Smyth
2025· review· en· Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
From the Editors
Anne Wojtak, Neil Stuart
2023· article· en· Healthcare Quarterly· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Commentary: Nurses Lead the Way...
Sheila Gallagher
2004· letter· en· Nursing leadership· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
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