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Record W2802106337 · doi:10.15171/ijhpm.2018.39

"First, Do No Harm": Have the Health Impacts of Government Bills on Tax Legislation Been Assessed in Finland?

2018· article· en· W2802106337 on OpenAlexaff
Natassa Aaltonen, Miisa Chydenius, Lauri Kokkinen

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLegislationHarmGovernment (linguistics)Public economicsPopulation healthBusinessPublic healthHealth policyEnvironmental healthPopulationEconomicsPolitical scienceEconomic growthHealth careMedicineLaw

Abstract

fetched live from OpenAlex

As taxation is one of the key public policy domains influencing population health, and as there is a legal, strategic, and programmatic basis for health impact assessment (HIA) in Finland, we analyzed all 235 government bills on tax legislation over the years 2007-2014 to see whether the health impacts of the tax bills had been assessed. We found that health impacts had been assessed for 13 bills, bills dealing with tobacco, alcohol, confectionery, and energy legislation and that four of these impact assessments included impacts on health inequalities between social classes. Based on our theoretical classification, the health impacts of 40 other tax bills should have been evaluated.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
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 integrity0.0000.001
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 head0.366
GPT teacher head0.570
Teacher spread0.204 · 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 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

Citations16
Published2018
Admission routes1
Has abstractyes

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