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Record W4412936229 · doi:10.1302/3114-251052

Training in Canada

2025· dataset· en· W4412936229 on OpenAlexaboutno aff

Bibliographic record

VenueOrthoMedia · 2025
Typedataset
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

The speaker, Pierre Guy, a member of the Canadian Orthopedic Association, discusses the challenges facing trainees in Canada's healthcare system during a conference. He emphasizes the need for perspectives on training constraints and the necessity for diversity, equity, and inclusion in the medical field to enhance the experiences and outcomes of future surgeons. He highlights the inadequate number of surgical positions relative to patient demand, pointing out the financial burdens trainees face due to long training durations, insufficient income, and high levels of student debt. The speaker contrasts training experiences and financial realities between Canada and the UK, showcasing the dramatic differences in income and debt incurred by medical students. Additionally, he discusses the pressing issue of housing affordability in urban centers like Vancouver and Toronto, which places further pressure on trainees. The speaker also elaborates on the importance of incorporating diversity and inclusion into healthcare practices, asserting that the orthopedic profession does not adequately reflect the diversity of the Canadian population. He advocates for early outreach programs aimed at encouraging young individuals, particularly women, to enter the field, and stresses the necessity of adapting recruitment processes to be more equitable and inclusive. Throughout his talk, he makes a case for improved support for trainees, enhanced collaboration with government authorities, and initiatives that celebrate the achievements of diverse practitioners within the orthopedic community.

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)0.000
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.029
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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