MétaCan
Menu
← Back to cohort
Record W2409353192

[The Internet:an alternative to face-to-face training for teachers in remote locations?].

2008· article· en· W2409353192 on OpenAlexaff
Suzanne Gagnon, Cassian Minguet

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
Field
Topic
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThe InternetTraining (meteorology)Distance educationSupervisorMedical educationFace-to-faceRural areaFace (sociological concept)VideoconferencingInternet accessComputer scienceWeb applicationMultimediaMedicinePsychologyWorld Wide WebPedagogyManagement
DOInot available

Abstract

fetched live from OpenAlex

PROBLEM ADDRESSED: For some family medicine supervisors working in rural and remote areas, access to face-to-face training is problematic. They need distance training programs designed specifically for them. OBJECTIVE OF PROGRAM: To study the advantages, disadvantages, and feasibility of a training program for these supervisors that is delivered over the Internet. PROGRAM DESCRIPTION: This was a pilot project for international on-line training consisting of a platform of courses and a collaborative type of Web conferencing that ran for 2 hours each week for 5 weeks. The training focused on the acquisition of teaching skills and the use of information and communications technology, and included discussions on topics related to practising and teaching in rural areas. CONCLUSION: Such a program is feasible and economical. The main difficulties are recruiting participants, keeping them in the program, and the amount of time spent on development and supervision. Participants who persevered reported high levels of satisfaction. The content of this type of training, barriers to participation, and the role of distance education in rural supervisor training programs remain to be explored.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0420.010

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 head0.115
GPT teacher head0.309
Teacher spread0.194 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→French-language works237,207→