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Record W2123241207 · doi:10.12927/hcpol.2014.23849

Accountability and Primary Healthcare

2014· article· fr· W2123241207 on OpenAlexafffundvenueabout
Shaheena Mukhi, Jan Barnsley, Raisa Deber

Bibliographic record

VenueHealthcare policy · 2014
Typearticle
Languagefr
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute for Work & HealthInstitute of Health Services and Policy ResearchUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Medical Association
KeywordsAccountabilityCorporate governanceHealthcare policyBusinessPrimary careHealth careHealthcare servicePublic relationsPublic administrationPolitical scienceMedicineHealth policyHealth care reformFamily medicineFinance

Abstract

fetched live from OpenAlex

This paper examines the accountability structures within primary healthcare (PHC) in Ontario; in particular, who is accountable for what and to whom, and the policy tools being used. Ontario has implemented a series of incremental reforms, using expenditure policy instruments, enforced through contractual agreements to provide a defined set of publicly financed services that are privately delivered, most often by family physicians. The findings indicate that reporting, funding, evaluation and governance accountability requirements vary across service provider models. Accountability to the funder and patients is most common. Agreements, incentives and compensation tools have been used but may be insufficient to ensure parties are being held responsible for their activities related to stated goals. Clear definitions of various governance structures, a cohesive approach to monitoring critical performance indicators and associated improvement strategies are important elements in operationalizing accountability and determining whether goals are being met.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.327
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.020
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.449
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2014
Admission routes4
Has abstractyes

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