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Record W4245433225 · doi:10.12927/hcpol.2018.25493

Centralized Waiting Lists for Unattached Patients in Primary Care: Learning from an Intervention Implemented in Seven Canadian Provinces

2018· article· en· W4245433225 on OpenAlexafffundvenueabout
Mylaine Breton, Sabrina T. Wong, Mélanie Ann Smithman, Sara A. Kreindler, Jalila Jbilou, Emily Gard Marshall, Jason M. Sutherland, Astrid Brousselle, Jay Shaw, Valorie A. Crooks, Damien Contandriopoulos, Martin Sasseville, Michael Green

Bibliographic record

VenueHealthcare policy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHôpital Charles-Le MoyneUniversité de SherbrookeUniversity of VictoriaMichael Smith Health Research BCSimon Fraser University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchUniversité de SherbrookeQueen's UniversityUniversité de MonctonMichael Smith Health Research BCResearch Manitoba
KeywordsPrimary careIntervention (counseling)Waiting listMedicineMedical emergencyComputer scienceNursingFamily medicineSurgery

Abstract

fetched live from OpenAlex

Centralized waiting lists (CWLs) are one solution to reduce the problematic number of patients without a regular primary care provider. This article describes different models of CWLs for unattached patients implemented in seven Canadian provinces and identifies common issues in the implementation of these CWLs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.466
Teacher spread0.407 · 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 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

Citations10
Published2018
Admission routes4
Has abstractyes

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