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Record W2059332746 · doi:10.2196/jmir.1938

Telemedicine Service Use: A New Metric

2012· article· en· W2059332746 on OpenAlexaff
Maurice Mars, Richard E. Scott

Bibliographic record

VenueJournal of Medical Internet Research · 2012
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTelemedicineTelecareTeleradiologyeHealthMetric (unit)Service (business)MedicineMedical emergencyHealth careBusinessMarketingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Policy makers and funding agencies require relevant information on current practices of the use of telemedicine infrastructure and services. Several metrics have been used to describe average use of telemedicine services. None are adequate. OBJECTIVE: To identify and assess a new metric-consultations per site per week (C/S/W). METHODS: To determine existing usage, all papers and abstracts published between January 2005 and December 2009 in the Journal of Telemedicine and Telecare and Telemedicine Journal and eHealth were reviewed. Pilot studies, research projects, services reporting less than one year's data and teleradiology services were excluded. RESULTS: In total, 210 reports of telemedicine services were identified, 77 of which provided sufficient data to calculate C/S/W. Average use was low, 1.8±3.5 (median 0.7) C/S/W, with 61% of services reporting less than 1 C/S/W and 71% reporting 2 or fewer C/S/W. Studies reporting on data from 2006 to 2009 showed less use (average 1.5±2.3; median 0.7 C/S/W) than earlier reports from 1996 to 2005 (1.7±2.5; median 0.7 C/S/W). CONCLUSIONS: The use of this new metric, C/S/W, is proposed as a standard measure of telemedicine service use. The generally low results opens debate about how well current clinical services are used.

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)0.011
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.224
GPT teacher head0.526
Teacher spread0.302 · 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 designNot applicable
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

Citations26
Published2012
Admission routes1
Has abstractyes

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