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Record W2098491714 · doi:10.2190/2fen-aqkk-lcev-7ku5

Capitation and Primary Care in Canada: Financial Incentives and the Evolution of Health Service Organizations

2001· article· en· W2098491714 on OpenAlexaffabout
James Gillett, Brian Hutchison, Stephen Birch

Bibliographic record

VenueInternational Journal of Health Services · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCapitationIncentiveRestructuringIncentive programBusinessPaymentService (business)Health careFinanceEconomic growthPublic relationsPolitical scienceEconomicsMarketing

Abstract

fetched live from OpenAlex

Alternative approaches to the funding, organization, and delivery of primary care have been the subject of ongoing discussion and debate in many industrialized nations for many years. One common recommendation has been to use capitation, as opposed to fee-for-service, as the payment method for physicians. In this study the authors use data from interviews with physicians and Ministry of Health officials to trace the evolution of Ontario's Health Service Organization (HSO) program, the only program of capitation-funded physician care in Canada. The program has developed in three phases: formation in the early 1970s, expansion in the late 1970s and throughout the 1980s, and restructuring in the 1990s. The analysis focuses on the perceptions and actions of policymakers and physicians who became involved with the program at different points in its evolution, and identifies how they perceived and responded to the financial incentives that were introduced to promote the program. This case study allows an examination of the shifting objectives, communications, perceptions, and responses of policymakers and stakeholders in changing contexts over a period of more than 20 years. The long history of the HSO program provides the opportunity to examine the factors that can cause financial incentives to go awry. The authors suggest how this case study offers lessons for financial incentive policymaking.

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 score0.373
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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

Citations20
Published2001
Admission routes2
Has abstractyes

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