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The effect of enrolment policies on patient affiliation to a family physician: A quasi-experimental evaluation in Canada

2025· article· en· W4409173101 on OpenAlexafffundabout
Caroline King, M. Ruth Lavergne, Kimberlyn McGrail, Erin Strumpf

Bibliographic record

VenueHealth Policy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcGill UniversityUniversity of British ColumbiaDalhousie UniversityInstitut National d'Excellence en Santé et en Services Sociaux
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCUniversité de SherbrookeMcGill University
KeywordsFamily medicinePsychologyMedicineGerontology

Abstract

fetched live from OpenAlex

• Patient enrolment with a source of primary care should support continuity of care. • The impact of enrolment policies on affiliation is an important intermediate outcome. • Our Reporting a Regular Medical Doctor index adds the patient view on affiliation. • Quebec enrolment policies did not improve three measures of affiliation. • Enrolment alone is insufficient to increase affiliation, a key input to continuity. Affiliation, defined as having a usual source of care, revealed by patterns of repeated interactions between the patient and a clinician over time, can influence patients’ care experience, continuity of care and health outcomes. Many jurisdictions implement primary care enrolment policies, with the motivation to increase affiliation and thereby improve downstream patient outcomes. However, there is little evidence on the impacts of these policies on patient-physician affiliation. Using health administrative data, we evaluated the population-level effects of two policies that encourage primary care enrolment on affiliation in Quebec, Canada. We used quasi-experimental study designs (difference-in-difference and interrupted-time-series) to estimate changes in affiliation that could be attributed to the introduction of these policies. The 2003 policy targeted the enrolment of elderly and/or chronically ill patients, whereas the 2009 policy targeted the general population. We used three measures of patient-physician affiliation: dichotomous and continuous usual provider continuity, and the Reporting a Regular Medical Doctor (RRMD) index. Our analyses for both policies did not yield substantively important changes in our outcomes at the population level. Our effect estimates for both policies were stable under several robustness checks specific to each method. Our results suggest that policies that encourage enrolment do not, on their own, have an impact on patient-physician affiliation. If enrolment policies are not sufficient to increase patient-physician affiliation, further research is needed to understand the factors that influence both affiliation and other downstream outcomes.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.348
Teacher spread0.315 · 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 designTheoretical or conceptual
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
Published2025
Admission routes3
Has abstractyes

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