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Record W4383683304 · doi:10.58931/cdet.2023.1214

Improving Access to Endocrinologists through Provider-to-Provider eConsultations

2023· article· en· W4383683304 on OpenAlexaff
Clare Liddy

Bibliographic record

VenueCanadian Diabetes & Endocrinology Today · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsSpecialtyHealth careBusinessInternet privacyMedicineNursingFamily medicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Access to specialty healthcare care remains a major issue for many Canadians. Not only are wait times long, but other barriers contribute to inequitable access. These include the patient’s ability to attend appointments (e.g. related to transportation difficulties and/or cost), and the ability of some patients to participate in an appointment (e.g. due to cognitive impairment, mobility challenges, loss of wages, or degree of comfort with a new provider). Informal consultations between healthcare providers have always played a role in healthcare access. The “call a colleague” approach works well when a practitioner is able to contact a colleague by telephone and an established network of specialists exists; however, it is inefficient, does not facilitate record-keeping for medico-legal purposes and follow-up, and is not remunerated. One way to help address these obstacles is through inter-provider electronic consultations (eConsults).

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.007

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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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