MétaCan
Menu
Back to cohort
Record W2137852612 · doi:10.12927/hcpap..17359

Bringing Healthcare Closer to Home: One Province's Approach to Home Care

2000· letter· en· W2137852612 on OpenAlexaffvenueabout
Elizabeth Witmer

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2000
Typeletter
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsOntario Long Term Care AssociationMinistry of Health and Long Term Care
Fundersnot available
KeywordsGovernment (linguistics)Long-term careHealth careBusinessReferralFront lineEconomic growthNursingPublic administrationMedicineGeographyPolitical science

Abstract

fetched live from OpenAlex

Ontario is implementing a number of steps to address the growing need for home care and continuing care. One of these steps is the establishment of Ontario's network of 43 Community Care Access Centres (CCACs). Responsible for aiding Ontario residents who seek community-based long-term healthcare, CCACs coordinate access to home services such as nursing and homemaking, manage placement to long-term care facilities and provide information and referral services. In 2000/01 the Ontario government announced 92.5 million Canadian dollars in new funding for long-term community services. This new funding includes 70.1 million Canadian dollars for CCACs. During this time, the provincial government will spend more than 1.6 billion Canadian dollars for long-term-care community-based services. Of this amount, 1.1 Canadian dollars billion will go to CCACs. Community Care Access Centres served more than 400,000 people in 1998/99 and are estimated to serve more than 420,000 in 2000/01. The administrative funds saved by this province-wide system are reinvested in front-line health services.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.002

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 head0.087
GPT teacher head0.345
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations8
Published2000
Admission routes3
Has abstractyes

Explore more

Same venueA Nudge Too Far? A Nudge at All? On Paying People to Be HealthySame topicHealthcare innovation and challengesFrench-language works237,207