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Record W2141171228 · doi:10.1177/0148607110376199

Developing a Mentoring Program in Clinical Nutrition

2010· article· en· W2141171228 on OpenAlexaff
Robert G. Martindale, Stephen A. McClave, Daren K. Heyland, David A. August

Bibliographic record

VenueJournal of Parenteral and Enteral Nutrition · 2010
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedical educationSet (abstract data type)Socratic methodMedicineClinical nutritionComputer sciencePolitical sciencePathology

Abstract

fetched live from OpenAlex

Mentoring programs in nutrition are essential to the survival of clinical nutrition as we know it today. The best method known to maintain an influx of talent to a discipline is by developing an active mentoring program. This paper describes 1 concept for development of a viable mentor program. Mentoring should be flexible and based on mentees' training background. Realistic goals should be set, with written and verbal feedback, to sustain a successful program. Programs should incorporate the Socratic Method whenever possible. Factors that leave doubt about the survival of nutrition as a viable area of focus for physicians include the inability to generate adequate funds to support oneself and limited numbers of mentors available with dedicated time to be a mentor. A healthy, sustainable mentoring program in clinical nutrition will ensure survival of physician-based nutrition programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.410
Teacher spread0.348 · 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 designObservational
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

Citations4
Published2010
Admission routes1
Has abstractyes

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