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Record W2768479221 · doi:10.12927/hcq.2017.25294

Physician Remuneration for Remote Consults: An Overview of Approaches across Canada

2017· article· en· W2768479221 on OpenAlexaffabout
Kelly Stanistreet, J Verma, Kirby Kirvan, Neil Drimer, Clare Liddy

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsBruyèreCanadian Foundation for Healthcare Improvement
Fundersnot available
KeywordsRemunerationGeneral partnershipSpecialtyMedicineHealth carePrimary careNursingQuality managementTelehealthQuality (philosophy)Family medicineTelemedicineMedical educationMedical emergencyBusinessPolitical science

Abstract

fetched live from OpenAlex

While lengthy waits for medical specialists remains a persistent problem across Canada, remote consult presents a strategy to address this issue. Connecting primary healthcare providers to specialists via electronic (eConsult) or telephone consult enables care providers to deliver appropriate, speciality-informed care for their patients in the primary care setting, reducing the time spent waiting for specialists and potentially preventing unnecessary referrals to specialty care. These remote consult models are the focus of a new pan-Canadian quality improvement collaborative delivered by the Canadian Foundation for Healthcare Improvement in partnership with Canada Health Infoway, the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada. Successful implementation of remote consult services requires alignment of remuneration for physicians. This article presents an overview of compensation arrangements across Canada for remote (telephone or electronic) and select in-person consults. It also shares key messages for payers and providers to inform future direction in this area.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.355
Teacher spread0.203 · 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 designOther design
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

Citations6
Published2017
Admission routes2
Has abstractyes

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