MétaCan
Menu
Back to cohort
Record W3124436994 · doi:10.55016/ojs/sppp.v7i1.42464

Ontario’s Experiment with Primary Care Reform

2014· article· en· W3124436994 on OpenAlexaffabout
Arthur Sweetman, Gioia Buckley

Bibliographic record

VenueThe School of Public Policy Publications · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPrimary carePrimary (astronomy)Political scienceMedicineFamily medicinePhysicsAstronomy

Abstract

fetched live from OpenAlex

For the past decade-and-a-half, the government of Ontario has been implementing sweeping reforms in an effort to improve primary health care delivery. Altering physician-compensation models is central to this initiative. One measure of the scale of change is that in 2000 roughly 95 per cent of general/family practitioners were paid traditional fee-for-service, but by 2013 that proportion had plunged to just 28 per cent. The province has clearly succeeded in largely replacing the traditional fee-for-service payment structure with blended payment models that are mostly group-oriented and include: 1) capitation (in some cases): a single payment for providing a particular “basket” of services to a patient for a fixed period, for example a year, regardless of the number of services provided, 2) fee-for-service payment, for services outside the capitated basket and provided in special situations, and 3) various bonuses and incentives (sometimes called pay-for-performance) that mostly focus on preventive care and the management of chronic conditions. Physicians in rural and northern areas, as well as some clinics, also have salary and similar models as options. Ontario has simultaneously introduced patient “rostering” — the formalized connecting of one patient to one physician and/or physician team/group — creating a relationship better suited to delivering preventive healthcare services. However, when surveyed, many patients are unaware that they have been “rostered” meaning that at present much of the benefit must be derived from the physician side alone. It remains to be seen whether or not it is important for patients to be aware that they are rostered. Beyond its clinical benefits, rostering has appreciable rhetorical and political value, as well as potential as a planning tool in efforts to ensure that the local and provincial supply of primary care is appropriate. In a health-care system as large and complex as Ontario’s, reform is more evolutionary than revolutionary; but the province has arguably moved rapidly within this context. Expenditures have been substantial and the initiatives groundbreaking. However, the same challenges that make reform a formidable undertaking also make it difficult to readily, or quickly, measure success, especially since many changes are ongoing. It is not yet demonstrably clear to what degree the government’s goals are being achieved. At present, there are mixed and conflicting findings about whether some of these changes have moved the health system towards the intended goals of improving health-care access and quality, and patient satisfaction, let alone whether the potential improvements can justify the resources expended to achieve them. Naturally, those results we do have at this point offer insight only into the short-term effects of these changes. Especially, it is too early for sufficient evidence to have accumulated on the impact of new physician-group models on downstream costs, including drug prescriptions, specialist care, hospital costs and the use of diagnostic tests. These are, however, central questions that will in large part determine success. Also, it appears that the Ontario government could have accomplished nearly all of its goals so far without having implemented capitation, although capitation may prove beneficial in the longer term as the scarcity of physicians since the 1990s seems to be shifting towards a surplus. In this new era, the health ministry will likely need to take a more hands-on role than it has in the past, including improved system monitoring. Going forward many stakeholders should be involved in evaluating this experiment on an ongoing basis to ensure that it is serving the healthcare needs of the population in an effective and efficient way..

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.399
Teacher spread0.337 · 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 designNot applicable
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

Citations25
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueThe School of Public Policy PublicationsSame topicPrimary Care and Health OutcomesFrench-language works237,207