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Record W2066094758 · doi:10.3138/ptc.59.4.255

Shifting the Public-Private Mix: A Policy Analysis of Physical Therapy Funding in Ontario

2007· article· en· W2066094758 on OpenAlexvenueaboutno aff
Michel D. Landry, A. Paul Williams, Molly C. Verrier, David Zakus, Raisa Deber

Bibliographic record

VenuePhysiotherapy Canada · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentBusinessQuality (philosophy)Public fundingPublic policyPublic economicsPublic relationsPublic administrationFinancePolitical scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Purpose: Physical therapy (PT) services across Canada are funded through a mix of public and private sources; this mix varies widely and appears to be shifting over time. The purpose of this research was to develop a conceptual framework for documenting and analyzing the public-private mix of funding in Ontario. Methods: Policy case study methodology was used and included triangulation of primary (key informant interviews) and secondary data sources (review of the literature and available documents). Results: We identified three broad categories or tiers for PT funding in Ontario: (1) public, (2) quasipublic, and (3) private. Within these tiers, there are multiple funding streams ranging from global budgets in hospitals to private out-of-pocket payments. Conclusions: Our findings suggest that multiple funding streams, along with significant variation in how clients access PT services and what services physical therapists can provide within each stream, result in a highly fractured and complex public/private funding system, which may have implications for cost, quality, and access.

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 score0.162
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.427
Teacher spread0.377 · 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
Published2007
Admission routes2
Has abstractyes

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