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Record W2599152101

Curriculum development of 6for6

2016· article· en· W2599152101 on OpenAlexaffvenueabout
Patti McCarthy, Cheri Bethune, Shari Fitzgerald, Wendy Graham, Shabnam Asghari, Thomas Heeley, Marshall Godwin

Bibliographic record

VenueCanadian Family Physician · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCurriculumMedical educationFoundation (evidence)Research programComputer sciencePhase (matter)Needs assessmentLongitudinal studyMedicinePsychologySociologyPedagogyPolitical sciencePathology
DOInot available

Abstract

fetched live from OpenAlex

Problem addressed To address barriers challenging the engagement of rural and remote family physicians (RRFPs) in research, Memorial University of Newfoundland in St John’s has developed a longitudinal faculty development program (FDP) called 6for6 . Objective of program To establish and evaluate a longitudinal FDP that promotes a foundation of research activity. Program description Informed by a needs assessment in phase 1, phase 2 saw the 6for6 curriculum designed, developed, and implemented to reflect the unique needs of RRFPs. Preliminary evaluations have been conducted and results will be presented after year 1 of the program. Conclusion The 6for6 FDP has been positively received by participants, and it is evident that they will serve as champions of rural research capacity building. It is anticipated that by April 2017, 18 RRFPs will be equipped with the research and leadership skills required to foster research networks within and outside their communities.

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)0.008
metaresearch head score (Gemma)0.010
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: Methods · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.005

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 head0.038
GPT teacher head0.356
Teacher spread0.318 · 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
GenreMethods

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

Citations1
Published2016
Admission routes3
Has abstractyes

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