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
Mentoring and Academic Development
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.

865 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.
865 works in the cohort · of 4,299,418page 3 of 18

Labels cover 5 of 865 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 865 of 865 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
One Click Away: Digital Mentorship in the Modern Era
Michael Gottlieb, Abra Fant, Andrew King, Anne Messman, Daniel Robinson, Guy Carmelli +1 more
2017· review· en· Cureus· Psychology
machine prediction:candidate · noneconsensus · none
32
citations
affunlabeled
Mentoring and networking
Catherine Tracey, Honor Nicholl
2006· review· en· Nursing Management· Psychology
machine prediction:candidate · noneconsensus · none
26
citations
afffundno abstractunlabeled
Developing a Mentorship Program for Psychiatry Residents
Sophie Soklaridis, Jenna López, Nate Charach, Kathleen Broad, John Teshima, Mark Fefergrad
2014· article· en· Academic Psychiatry· Psychology
machine prediction:candidate · noneconsensus · none
25
citations
affaboutunlabeled
Mentoring experiences of successful women across the Americas
Silvia Inés Monserrat, Jo Ann Duffy, Miguel R. Olivas‐Luján, John M. Miller, Ann Gregory, Suzy Fox +3 more
2009· article· en· Gender in Management An International Journal· Psychology
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
NextGen Voices: Quality mentoring
Lauren Segal, Divyansh Agarwal, Kyle J. Isaacson, Theresa B. Oehmke, Brijesh Kumar, Jennifer Chen +25 more
2018· letter· en· Science· Psychology
machine prediction:candidate · noneconsensus · none
23
citations
affaboutunlabeled
Key Considerations for Advancing Women in Coaching
Jenessa Banwell, Gretchen Kerr, Ashley Stirling
2019· article· en· Women in Sport and Physical Activity Journal· Psychology
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Developmental Approaches to Faculty Development
John Teshima, Alastair J. McKean, Myo Thwin Myint, Shadi Aminololama‐Shakeri, Shashank V. Joshi, Andreea L. Seritan +1 more
2019· review· en· Psychiatric Clinics of North America· Psychology
machine prediction:candidate · noneconsensus · none
23
citations

How this was built: Screen · Findings · About