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Record W2114423289 · doi:10.1071/nb10072

Population Health Intervention Research Initiative for Canada: progress and prospects

2011· article· en· W2114423289 on OpenAlexafffundabout
Penelope Hawe, Stephen Samis, Erica Di Ruggiero, Jean Shoveller

Bibliographic record

VenueNew South Wales Public Health Bulletin · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitute of Population and Public HealthCanadian Foundation for Healthcare ImprovementThe Quebec Population Health Research NetworkUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesInstitute of Population and Public HealthMichael Smith Health Research BCUniversity of TorontoFondation pour la Recherche Médicale
KeywordsRedressIntervention (counseling)Population healthPopulationPublic relationsHealth policyPolitical scienceEconomic growthBusinessMedicinePublic healthNursingEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Actions in Canada are being designed to transform the way research evidence is generated and used to improve population health. Capacity is being built in population health intervention research. The primary target is more understanding and examination of policies and programs that could redress inequities in health. The Population Health Intervention Research Initiative for Canada is a loosely-networked collaboration designed to advance the science of the field as well as the quantity, quality and use of population health intervention research to improve the health of Canadians. In the first few years there have been new training investments, new funding programs, new working guidelines for peer review, symposia and new international collaborations. This has been brought about by the strategic alignment of communication, planning and existing investments and the leveraging of new resources.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.785
GPT teacher head0.641
Teacher spread0.143 · 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

Citations10
Published2011
Admission routes3
Has abstractyes

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