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Record W2119985240 · doi:10.12927/hcpap.2015.24102

Transformation through Clinical and Social Integration: Meeting the Needs of High Users of Healthcare

2014· article· en· W2119985240 on OpenAlexaffvenueabout
Jérémy Veillard, Keith Denny

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCarleton UniversityCanadian Institute for Health Information
Fundersnot available
KeywordsHealth careTransformation (genetics)Social careComputer scienceNursingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

A minority of patients consume the bulk of health services and/or the costs of care. This group provides a focus for a number of concerns related to health system sustainability, the appropriateness and effectiveness of care and the proportion of government program spending made up by health expenditures. This introduction offers five observations. First, if Ontario's Health Links are to meet the needs of high users, local autonomy may have to be balanced with more consistent frameworks. Second, there is a need for creative approaches to evaluation, specifically in the area of rapid cycle evaluation. Third, genuine innovation will require clear role specifications in governance relationships and bold approaches to accountability that build in space for learning from "good" failure. Fourth, successful interventions will encompass social care services and broader social determinants as well as clinical factors and, fifth, we will need an approach to stewardship that facilitates intersectoral action.

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)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.014
Scholarly communication0.0120.006
Open science0.0020.016
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.088
GPT teacher head0.453
Teacher spread0.365 · 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 designQualitative
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

Citations1
Published2014
Admission routes3
Has abstractyes

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