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Record W1967854705 · doi:10.2196/jmir.5.1.e3

A Model for Online Interactive Remote Education for Medical Physics Using the Internet

2003· article· en· W1967854705 on OpenAlexaffabout
M Woo, Kwan-Hoong Ng

Bibliographic record

VenueJournal of Medical Internet Research · 2003
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsThe InternetProcess (computing)Physics educationMedical educationMultimediaComputer scienceWorld Wide WebMathematics educationMedicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Medical physics is a relatively small community but it spans great geographical distances, usually with a scarcity of experts whose expertise could greatly benefit students entering into the field. In addition there are many software systems for which an interactive education method would be most advantageous. OBJECTIVE: To develop a process to optimally use the Internet for real-time interactive remote education of medical physics and to present the experience of the study. METHODS: The project is a collaboration of the Department of Medical Physics at the Toronto-Sunnybrook Regional Cancer Centre in Canada and the Department of Radiology at the University of Malaya in Malaysia. A class of medical-physics graduate students at the University of Malaya attended lectures provided by lecturers in Toronto, using the Internet as the main tool of communication. RESULTS: The different methods that can be used to provide the real-time interactive remote education were explored, and various topics-including traditional classroom lectures as well as hands-on workshops-were also delivered. CONCLUSIONS: The concept of real-time interactive remote education is viable and holds promise for providing economical and practical tele-education to the medical physics community, but depends heavily on the availability of the Internet in many developing countries.

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)0.006
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.158
GPT teacher head0.581
Teacher spread0.423 · 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.

Study designSimulation or modeling
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

Citations18
Published2003
Admission routes2
Has abstractyes

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