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Whatever happened to regionalization? The curious case of Nova Scotia

2006· article· en· W2056261492 on OpenAlexaffabout
Martha Black, Katherine Fierlbeck

Bibliographic record

VenueCanadian Public Administration · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNova scotiaContradictionContext (archaeology)DecentralizationHealth careAppealJurisdictionAccountabilityPolitical sciencePoliticsPublic administrationHumanitiesGeographySociologyLawArtEthnology

Abstract

fetched live from OpenAlex

Abstract: The case of Nova Scotia well illustrates the complexities involved in implementing a strategy of regionalization in health care. In 1996, under the leadership of Liberal Premier John Savage, thirty‐six local hospital boards were amalgamated into four regional health boards. By 2001, however, Conservative Premier John Hamm had expanded the four regions into nine district health authorities. Both measures were justified by explicit references to cost containment and greater accountability, even though the first took numerous units and amalgamated them, while the second took the few units and multiplied them. How can this seeming contradiction be explained, and what does it say about the nature of regionalization as a policy tool for health care? The authors find that neither cost containment nor citizen engagement can explain the system of regionalization which currently informs the health care system in Nova Scotia. Rather, the present form of regionalization exists because it is useful politically in two ways: it maintains the centralization of power that existed previous to the formal decentralization of health care; and it restores the system of representation that existed prior to the implementation of regionalization. The authors conclude that, to understand how regionalization has been implemented in any given jurisdiction, one must pay close attention to the political context in which strategies of regionalization have been executed. Sommaire: Le cas de la Nouvelle‐Écosse illustre bien les complexités inhérentes à la mise en œuvre d'une stratégie de régionalisation dans les soins de santé. En 1996, sous le leadership du Premier ministre libéral John Savage, 36 conseils d'hôpitaux locaux ont fusionné pour former quatre conseils de sante régionaux. En 2001, cependant, le Premier ministre conservateur John Hamm a élargi les quatre régions pour les transformer en neuf conseils de santé de district. Ces deux mesures ont été justifiées par des références explicites à la compression des coûts et à une plus grande imputabilité, même si la première a consistéà prendre de nombreuses unités et à les fusionner, tandis que la seconde a consistéà prendre quelques rares unités et à les multiplier. Comment peut‐on expliquer cette apparente contradiction, et qu'est‐ce que cela nous dit sur la nature de la régionalisation en tant qu'outil de politique en matière de soins de santé? Les auteurs trouvent que ni la compression des coûts, ni la participation des citoyens ne peuvent expliquer le système de régionalisation qui caractérise actuellement le système de soins de santé en Nouvelle‐Écosse. Au contraire, la forme actuelle de régionalisation existe parce qu'elle est politiquement utile de deux manières: elle maintient la centralisation du pouvoir qui existait avant la décentralisation officielle des soins de santé; et elle restaure, jusqu'à un certain point, le système de représentation qui existait avant la mise en œuvre de la régionalisation. Les auteurs concluent que, pour comprendre la manière dont la régionalisation a été mise en œuvre, il faut prêter une grande attention au contexte politique dans lequel ces stratégies de régionalisation ont étéélaborées.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.392
Teacher spread0.346 · 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.

Study designNot applicable
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

Citations13
Published2006
Admission routes2
Has abstractyes

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