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Record W2512166791 · doi:10.1007/s40037-016-0292-2

A journal club for peer mentorship: helping to navigate the transition to independent practice

2016· article· en· W2512166791 on OpenAlexafffund
Thomas E. MacMillan, Shail Rawal, Peter Cram, Jessica Liu

Bibliographic record

VenuePerspectives on Medical Education · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSinai Health SystemUniversity of TorontoUniversity Health Network
FundersUniversity of Toronto
KeywordsMentorshipJournal clubMedical educationDilemmaMedicineTransition (genetics)Psychology

Abstract

fetched live from OpenAlex

The transition from residency to independent practice presents unique challenges for physicians. New attending physicians often have unmet learning needs in non-clinical domains. An attending physician is an independent medical practitioner, sometimes referred to as a staff physician or consultant. Peer mentorship has been explored as an alternative to traditional mentorship to meet the learning needs of new attendings. In this article, the authors describe how a journal club for general internal medicine fellowship graduates helped ease the transition by facilitating peer mentorship. Journal club members were asked to bring two things to each meeting: a practice-changing journal article, and a 'transition to practice' discussion topic such as a diagnostic dilemma, billing question, or a teaching challenge. Discussions fell into three broad categories that the authors have termed: trading war stories, measuring up, and navigating uncharted waters. It is likely that physicians have a strong need for peer mentorship in the first few years after the transition from residency, and a journal club or similar discussion group may be one way to fulfil this.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.004
metaresearch head score (Gemma)0.052
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.052
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 science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.406
Teacher spread0.383 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designOther design · Qualitative
Domainnot available
GenreEmpirical · Commentary

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

Citations36
Published2016
Admission routes2
Has abstractyes

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