MétaCan
Menu
Back to cohort
Record W4220910980 · doi:10.1093/polsoc/puac010

Health reforms and policy capacity: the Canadian experience

2022· article· en· W4220910980 on OpenAlexafffundabout
Jean‐Louis Denis, Susan Usher, Johanne Préval

Bibliographic record

VenuePolicy and Society · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversité de SherbrookeCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)Health policyLegislationPublic administrationCapacity buildingWork (physics)Political scienceCore (optical fiber)SociologyHealth careLawComputer science

Abstract

fetched live from OpenAlex

Abstract Recent work on health system strengthening suggests that a combination of leadership and policy capacity is essential to achieve transformation and improvement. Policy capacity and leadership are mutually constitutive but difficult to assemble in a coherent and consistent way. Our paper relies on the nested model of policy capacity to empirically explore how health reformers in seven Canadian provinces address the question of policy capacity. More specifically, we look at emerging representations of policy capacity within the context of health reforms between 1990 and 2020. Based on the exploration of the scientific and grey literature (legislation, annual reports of Ministries, agencies and organizations, meeting minutes, press, etc.) and interviews with key informants (n = 54), we identify how policy capacity is considered and framed within health reforms A series of core dilemmas emerge from attempts by each province to develop policy capacity for and through health reforms.

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)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0490.026
Scholarly communication0.0110.003
Open science0.0030.008
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0080.000

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 head0.047
GPT teacher head0.323
Teacher spread0.276 · 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 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

Citations24
Published2022
Admission routes3
Has abstractyes

Explore more

Same venuePolicy and SocietySame topicGlobal Public Health Policies and EpidemiologyFrench-language works237,207