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Record W3032837839 · doi:10.15173/ijsap.v4i1.3878

A collective education mentorship model (CEMM): Responding to the TRC calls to action in undergraduate Indigenous health teaching

2020· article· en· W3032837839 on OpenAlexaffvenue
Sharon Yeung, Yipeng Ge, Deepti Shanbhag, Alex X. Liu, Bernice Downey, Karen D. Hill, Dawn Martin‐Hill, Ellen Amster, Constance McKnight, Gita Wahi

Bibliographic record

VenueInternational Journal for Students as Partners · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of OttawaAssembly of First NationsUniversity of TorontoAboriginal Affairs Northern Dev CanadaMcMaster UniversityQueen's University
FundersMcKnight Foundation
KeywordsMentorshipIndigenousExperiential learningSupervisorReciprocity (cultural anthropology)Experiential educationMedical educationAction (physics)PedagogyPsychologyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

In this paper, a Collective Education Mentorship Model (CEMM) is described by four non-Indigenous students who co-created and undertook a program with this model for an undergraduate-level university experiential learning experience centred around Indigenous health. This model is framed around shared teaching of students by various collaborators/mentors and built upon the values of collaboration, mentorship, reciprocity, and capacity building. Based on feedback from the students and collaborators involved in this experience, this model appears to be a promising means of better situating students as partners in experiential learning through the redefinition of student-supervisor roles, responsibilities, and the sharing of power. Furthermore, this model appeared to create more diverse experiences for students and minimized supervisor burden. Although this model was created specifically for the education of trainees in Indigenous health, it can be further adapted for other student placements and programs where these assets would be beneficial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.178
GPT teacher head0.604
Teacher spread0.426 · 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 teacher head, not a consensus.

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

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

Citations1
Published2020
Admission routes2
Has abstractyes

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