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Record W2166382536 · doi:10.1258/1357633053499877

An incremental cost analysis of telehealth in Nova Scotia from a societal perspective

2005· article· en· W2166382536 on OpenAlexaffabout
D. David Persaud, Steve Jreige, Chris Skedgel, J. P. Finley, Joan Sargeant, Neil Hanlon

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2005
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Northern British ColumbiaDalhousie University
Fundersnot available
KeywordsTelehealthLiberian dollarWorkloadTeledermatologyPerspective (graphical)Nova scotiaMedicineTelemedicineBusinessCost–benefit analysisTelepsychiatryHealth careNursingPolitical scienceEconomicsComputer scienceGeographyFinanceEconomic growthManagement

Abstract

fetched live from OpenAlex

We examined the costs of telehealth in Nova Scotia from a societal perspective. The clinical outcomes of telepsychiatry and teledermatology services were assumed to be similar to those for conventional face-to-face consultations. Cost information was obtained from the Nova Scotia Department of Health, the Canadian Institute for Health Information, and questionnaires to patients, physicians and telehealth coordinators. There were 215 questionnaires completed by patients, 135 by specialist physicians and eight by telehealth coordinators. Patient costs for a face-to-face consultation ranged from $240 to $1048 (all costs in Canadian dollars), whereas patient costs for telehealth were lower, from $17 to $70. However, from a societal perspective, the overall cost of providing face-to-face services was lower than for telehealth: the total costs for face-to-face services ranged from $325 to $1133, while the total costs for telehealth services ranged from $1736 to $28,084. A threshold analysis showed that, above a certain patient workload, telehealth services would be more cost-effective than face-to-face services from a societal perspective. This workload is attainable in Nova Scotia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.386
Teacher spread0.358 · 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

Citations55
Published2005
Admission routes2
Has abstractyes

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