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Record W7094245158

Integrating Public Health into Local Healthcare Governance in Quebec: Challenges in Combining Population and Organization Perspectives

2009· article· en· W7094245158 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careCorporate governancePublic healthPublic healthcareArticulation (sociology)Healthcare systemPopulation
DOInot available

Abstract

fetched live from OpenAlex

The quest for greater efficiency in health systems encourages governments to bring together two fields of practice that have largely developed in parallel in industrialized countries: public health and healthcare. Current healthcare reform in the province of Quebec formally integrates these two fields within a common governance structure. The objective of this paper is to discuss the issues arising from the integration of public health services into the planning and delivery of local healthcare services, and its potential effect on the overall performance of the healthcare system. The authors begin by describing the characteristics of these two sectors; then, they discuss current reforms in Quebec and the impact of various transitions (epidemiological, technological and organizational) that bring the sectors into greater convergence. The paper concludes with a discussion of obstacles and potential opportunities at two levels: (a) the development of population-based planning of services within healthcare organizations, and (b) the articulation of public health and healthcare services concerns at the local level. The ongoing reform in Quebec is a unique opportunity to maximize outcomes from the resources invested in the healthcare system, based on a collective vision for improving health. This paper was originally published in French, in the journal Pratiques et organisation des soins 39(2): 113–24.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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