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Record W4389285468 · doi:10.1002/hec.4782

Effects of extending residencies on the supply and quality of family medicine practitioners; difference‐in‐differences evidence from the implementation of mandatory family medicine residencies in Canada

2023· article· en· W4389285468 on OpenAlexaffabout
Stephenson Strobel

Bibliographic record

VenueHealth Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsPrimary careFamily medicineLeverage (statistics)Quality (philosophy)MedicineResidency trainingPhysician supplyMEDLINEPatient careNursingMedical educationEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

I examine the impacts of extending residency training programs on the supply and quality of physicians practicing primary care. I leverage mandated extended residency lengths for primary care practitioners that were rolled out over 20 years in Canada on a province-by-province basis. I compare these primary care specialties to other specialties that did not change residency length (first difference) before and after the policy implementation (second difference) to assess how physician supply evolved in response. To examine quality outcomes, I use a set of scraped data and repeat this difference-in-differences identification strategy for complaints resulting in censure against physicians in Ontario. I find declines in the number of primary care providers by 5% for up to 9 years after the policy change. These changes are particularly pronounced in new graduates and younger physicians, suggesting that the policy change dissuaded these physicians from entering primary care residencies. I find no impacts on quality of physicians as measured by public censure of physicians. This suggests that extending primary care training caused declines in physician supply without improvement in the quality of these physicians. This has implications for current plans to extend residency training programs.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.165
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.238
GPT teacher head0.381
Teacher spread0.143 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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