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Record W1918805558 · doi:10.5334/ijic.1140

Mergers and integrated care: the Quebec experience

2013· article· en· W1918805558 on OpenAlexaffabout
Louis Demers

Bibliographic record

VenueInternational Journal of Integrated Care · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsBusinessContext (archaeology)Integrated careHealth careIncentiveMergers and acquisitionsService (business)Public relationsHealthcare serviceKnowledge managementMarketingEconomic growthPolitical scienceEconomicsComputer scienceFinanceMarket economy

Abstract

fetched live from OpenAlex

As a researcher, I have studied the efforts to increase the integration of health and social services in Quebec, as well as the mergers in the Quebec healthcare system. These mergers have often been presented as a necessary transition to break down the silos that compartmentalize the services dispensed by various organisations. A review of the studies about mergers and integrated care projects in the Quebec healthcare system, since its inception, show that mergers cannot facilitate integrated care unless they are desired and represent for all of the actors involved an appropriate way to deal with service organisation problems. Otherwise, mergers impede integrated care by creating increased bureaucratisation and standardisation and by triggering conflicts and mistrust among the staff of the merged organisations. It is then preferable to let local actors select the most appropriate organisational integration model for their specific context and offer them resources and incentives to cooperate.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.899
Threshold uncertainty score0.736

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0140.009
Scholarly communication0.0060.003
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.000

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 head0.049
GPT teacher head0.440
Teacher spread0.392 · 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 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
Published2013
Admission routes2
Has abstractyes

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