MétaCan
Menu
Back to cohort
Record W3168218757 · doi:10.26443/mjm.v19i1.830

A Medical Student's Perspective on "Fighting for a Hand to Hold"

2021· article· en· W3168218757 on OpenAlexaffvenueabout
Susan Joanne Wang

Bibliographic record

VenueMcGill Journal of Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousRacismColonialismHealth careMedicineHealth professionalsPerspective (graphical)Media studiesEconomic JusticeMedical educationSociologyGender studiesPolitical scienceVisual artsLawArt

Abstract

fetched live from OpenAlex

Fighting for a Hand to Hold by Dr. Samir Shaheen-Hussain is a heartbreaking and compelling read depicting the history of injustices, terror and trauma inflicted upon Indigenous children by the Canadian medical system. As an emergency pediatrician at the McGill University Health Centre and associate professor at McGill University, Dr. Shaheen-Hussain weaves his clinical experiences and long-standing advocacy efforts alongside archival research to shed insight on medical colonialism. This piece is structured in two parts: a book review followed by a personal reflection. It is accompanied by a podcast interview with Dr. Shaheen-Hussain in which he discusses his social justice work, his book, and advocacy advice for students in healthcare. This book review highlights the importance of Fighting for a Hand to Hold as a seminal piece of literature for all healthcare professionals and trainees across Canada. In the personal reflection, the author considers their own experiences with race and racism as a person of colour, settler Canadian, and medical student. This reflection concludes by advocating for more emphasis on Indigenous health in Canadian medical education and practice.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.163
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0240.020
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0040.001

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 head0.083
GPT teacher head0.499
Teacher spread0.415 · 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
GenreCommentary

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
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueMcGill Journal of MedicineSame topicChild and Adolescent HealthFrench-language works237,207