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Record W2104691876 · doi:10.1080/13561820701773041

Why mentorship matters: Students, staff and sustainability in interprofessional education

2008· article· en· W2104691876 on OpenAlexaffabout
Steven J. Hoffman, Alexandra Harris, Daniel Rosenfield

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsQueen's UniversityCanadian Public Health AssociationUniversity of Toronto
Fundersnot available
KeywordsMentorshipInterprofessional educationSustainabilityMedical educationHealth professionalsProfessional developmentPower (physics)MedicineHealth carePsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

As students in the Canadian education system, we have come to realize the true power that a great mentor can possess. Indeed, most health professional students can easily identify the reason they c...

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)0.021
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.181
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.019
Scholarly communication0.0180.008
Open science0.0020.009
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.018
GPT teacher head0.381
Teacher spread0.363 · 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 designQualitative
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

Citations12
Published2008
Admission routes2
Has abstractyes

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