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Record W2767539909 · doi:10.1377/hlthaff.2017.0014

In British Columbia, The Supply Of Primary Care Physicians Grew, But Their Rate Of Clinical Activity Declined

2017· article· en· W2767539909 on OpenAlexaffabout
Lindsay Hedden, Morris L. Barer, Kimberlyn McGrail, Michael R. Law, Ivy Lynn Bourgeault

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversité du QuébecCentre for Advancing Health Outcomes
Fundersnot available
KeywordsRemunerationWorkforcePrimary carePhysician supplyMedicineEconomic shortageFamily medicineDemographyPopulationFeminization (sociology)Primary care physicianSpecialtyCensusCohortGerontologyBusinessEnvironmental healthEconomic growthFinanceGovernment (linguistics)Economics

Abstract

fetched live from OpenAlex

Reports of a primary care shortage are ubiquitous in Canada and the United States. We used a population-based, retrospective cohort study to examine the extent to which the feminization and aging of the primary care physician workforce and secular trends may contribute to changes in the availability of primary care services. We used billing data for all primary care physicians in British Columbia for the period 2005-12. We used multivariate linear mixed-effects models to study physician remuneration and activity levels. We found limited change in per physician remuneration over the study period. However, numbers of patient contacts and practice sizes (numbers of unique patients) declined by 14 percent and 10 percent, respectively. Although the feminization of the workforce-and, to a lesser extent, its aging-contributed to this decline, the primary driver appears to be a broad trend toward reduced clinical activity over time. To the extent that similar trends are occurring in the United States, the implications of our study for the availability of primary care services beyond Canada are potentially significant.

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)0.002
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 score0.293
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.081
GPT teacher head0.331
Teacher spread0.250 · 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

Citations44
Published2017
Admission routes2
Has abstractyes

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