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Record W3215847988 · doi:10.9778/cmajo.20200235

Primary care bonus payments and patient-reported access in urban Ontario: a cross-sectional study

2021· article· en· W3215847988 on OpenAlexaffvenueabout
Kamila Premji, Ewa Sucha, Richard H. Glazier, Michael Green, Walter P. Wodchis, William Hogg, Tara Kiran, Eliot Frymire, Thomas R. Freeman, Bridget Ryan

Bibliographic record

VenueCMAJ Open · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsRuralityMedicineCross-sectional studyPaymentConfidence intervalOdds ratioOddsSurvey data collectionDemographyHealth careFamily medicineRural areaLogistic regressionBusinessFinanceStatistics

Abstract

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BACKGROUND: Rurality strongly correlates with higher pay-for-performance access bonuses, despite higher emergency department use and fewer primary care services than in urban settings. We sought to evaluate the relation between patient-reported access to primary care and access bonus payments in urban settings. METHODS: We conducted a cross-sectional, secondary data analysis using Ontario survey and health administrative data from 2013 to 2017. We used administrative data to calculate annual access bonuses for eligible urban family physicians. We linked this payment data to adult (≥ 16 yr) patient data from the Health Care Experiences Survey to examine the relation between access bonus achievement (in quintiles of the proportion of bonus achieved, from lowest [Q1, reference category] to highest [Q5]) and 4 patient-reported access outcomes. The average survey response rate to the patient survey during the study period was 51%. We stratified urban geography into large, medium and small settings. In a multilevel regression model, we adjusted for patient-, physician- and practice-level covariates. We tested linear trends, adjusted for clustering, for each outcome. RESULTS: We linked 18 893 respondents to 3940 physicians in 414 bonus-eligible practices. Physicians in small urban settings earned the highest proportion of their maximum potential access bonuses. Access bonus achievement was positively associated with telephone access (Q2 odds ratio [OR] 1.18, 95% confidence interval [CI] 0.98-1.42; Q3 OR 1.34, 95% CI 1.10-1.63; Q4 OR 1.46, 95% CI 1.19-1.79; Q5 OR 1.87, 95% CI 1.50-2.33), after hours access (Q2 OR 1.26, 95% CI 1.09-1.47; Q3 OR 1.46, 95% CI 1.23-1.74; Q4 OR 1.77, 95% CI 1.46-2.15; Q5 OR 1.88, 95% CI 1.52-2.32), wait time for care (Q2 OR 1.01, 95% CI 0.85-1.20; Q3 OR 1.17, 95% CI 0.97-1.41; Q4 OR 1.27, 95% CI 1.05-1.55; Q5 OR 1.63, 95% CI 1.32-2.00) and timeliness (Q2 OR 1.29, 95% CI 0.98-1.69; Q3 OR 1.29, 95% CI 0.94-1.77; Q4 OR 1.58, 95% CI 1.16-2.13; Q5 OR 1.98, 95% CI 1.38-2.82). When stratified by geography, we observed several of these associations in large urban settings, but not in small urban settings. Trend tests were statistically significant for all 4 outcomes. INTERPRETATION: Although the access bonus correlated with access in larger urban settings, it did not in smaller settings, aligning with previous research questioning its utility in smaller geographies. The access bonus may benefit from a redesign that considers geography and patient experience.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.108
GPT teacher head0.464
Teacher spread0.356 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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