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Record W2397003521 · doi:10.2105/ajph.2016.303224

Mobilizing Local Authorities Around Public Health Priorities

2016· article· en· W2397003521 on OpenAlexaffabout
Benoît Lévesque, Vicky Huppé, André Tourigny

Bibliographic record

VenueAmerican Journal of Public Health · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsGeneral partnershipPlan (archaeology)Public healthBusinessPublic administrationEnvironmental planningEnvironmental healthPolitical scienceGeographyMedicineNursing

Abstract

fetched live from OpenAlex

Large Analysis and Review of European Housing and Health Status (LARES) was conducted in Europe in 2002 to 2003 to study the relationship between citizens' health and built environments. One of its objectives was to put public health priorities on the agenda of local decision-makers to implement solutions for the community. We adapted the LARES protocol as a pilot project in a small French-Canadian town in Quebec Province in 2012. The distinguishing feature of this project was the collaborative approach taken with local actors, especially the municipality, which was committed a priori to using survey data from an urban planning perspective. The project produced interesting results that were used to motivate actions concerning people living in bad sanitary conditions; to draft the urban plan including the development of parks, green spaces, and bicycle paths; and to allow the municipality to meet eligibility criteria for access to renovation programs. If a partnership with the local actors and their commitment to promote and realize the project were obtained at the beginning, then the survey could be replicated in other communities.

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)0.070
metaresearch head score (Gemma)0.086
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: none
Teacher disagreement score0.070
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.007
Science and technology studies0.0080.005
Scholarly communication0.0130.010
Open science0.0040.031
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0220.003

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 head0.095
GPT teacher head0.341
Teacher spread0.246 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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