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Record W4380870852 · doi:10.1177/08404704231183174

An analysis of policies supporting the roles of family physicians in four regions in Canada during the COVID-19 pandemic

2023· article· en· W4380870852 on OpenAlexafffundabout
Maria Mathews, Leslie Meredith, Dana Ryan, Lindsay Hedden, Julia Lukewich, Emily Gard Marshall, Lauren Moritz, Sarah Spencer, Jennifer Xiao, Judith Belle Brown, Madeleine McKay, Eric Wong, Paul Gill

Bibliographic record

VenueHealthcare Management Forum · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsNova Scotia HospitalSt Joseph's Health CareDalhousie UniversityMemorial University of NewfoundlandSimon Fraser UniversityThames Valley Children's CentreWestern University
FundersCanadian Institutes of Health Research
KeywordsPandemicPreparednessCoronavirus disease 2019 (COVID-19)BusinessPublic policyVaccinationPublic healthControl (management)Vaccination policyPublic relationsNursingPolitical scienceMedicineDiseaseVirologyEconomicsManagement

Abstract

fetched live from OpenAlex

Policy supports are needed to ensure that Family Physicians (FPs) can carry out pandemic-related roles. We conducted a document analysis in four regions in Canada to identify regulation, expenditure, and public ownership policies during the COVID-19 pandemic to support FP pandemic roles. Policies supported FP roles in five areas: FP leadership, Infection Prevention and Control (IPAC), provision of primary care services, COVID-19 vaccination, and redeployment. Public ownership polices were used to operate assessment, testing and vaccination, and influenza-like illness clinics and facilitate access to personal protective equipment. Expenditure policies were used to remunerate FPs for virtual care and carrying out COVID-19-related tasks. Regulatory policies were region-specific and used to enact and facilitate virtual care, build surge capacity, and enforce IPAC requirements. By matching FP roles to policy supports, the findings highlight different policy approaches for FPs in carrying out pandemic roles and will help to inform future pandemic preparedness.

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 score0.113
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.085
GPT teacher head0.402
Teacher spread0.317 · 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

Citations10
Published2023
Admission routes3
Has abstractyes

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