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Record W2770020165 · doi:10.1093/heapro/dax081

Implementation of Health 2015 public health program in Finland: a welfare state in transition

2017· article· en· W2770020165 on OpenAlexaff
Lauri Kokkinen, Carles Muntaner, Patricia O’Campo, Alix Freiler, Golda Oneka, Ketan Shankardass

Bibliographic record

VenueHealth Promotion International · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsWilfrid Laurier UniversityPublic Health OntarioUniversity of Toronto
FundersSuomen Kulttuurirahasto
KeywordsPublic healthHealth promotionRestructuringHealth policyContext (archaeology)Welfare stateProcurementBusinessPublic economicsPublic administrationPolitical sciencePoliticsEconomic growthEconomicsMedicineNursingMarketingFinance

Abstract

fetched live from OpenAlex

Our study sought to examine the implementation of Health 2015 [a public health programme prepared following the principles of Health in All Policies (HiAP)] between 2001 and 2015 in the context of welfare state restructuring. We used data from the realist multiple explanatory case study by HARMONICS, which focused on political factors (processes) that lead to the (un)successful implementation of programmes following the principles of HiAP. We analyzed data-key informant interviews, grey and scholarly literature-from our Finnish case to examine how Health 2015 implementation has been affected by the changing role of the state. We find that the dismantling of formal funding allocation decreased the capacity of national authorities to exert control over municipalities' health promotion work, diluting the financial arrangements regarding municipal obligations. As a result, most municipalities failed to contribute to Health 2015, resulting in losses for health promotion activities. Our results also point to joining the EU. Whereas the procedures for preparing Finland's unanimous positions on EU matters were useful in harmonizing ideologies on various policy issues between different ministries, joining the EU also increased commercial interests and the strength of the lobby system, leading to the prioritization of economic objectives over public health objectives. Finally, our informants also highlighted the changing relationship between the state and the market, manifested in market deregulation and increasing influence of pro-growth arguments during the implementation of Health 2015.

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)0.004
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: none
Teacher disagreement score0.856
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.534
Teacher spread0.377 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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