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Record W1538790134

Social capital, health, and Francophone minorities.

2006· article· en· W1538790134 on OpenAlexaffabout
Louise Bouchard, Anne Gilbert, Rodrigue Landry, Kenneth Deveau

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité Sainte-AnneInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsSocial capitalSocial engagementVitalityEconomic growthSocial determinants of healthSocial network (sociolinguistics)Social supportSociologyBusinessPublic relationsPublic economicsPsychologyPolitical scienceHealth careEconomicsSocial psychologySocial scienceSocial media
DOInot available

Abstract

fetched live from OpenAlex

The goal of this article is to outline the analytical perspectives of the concept of social capital regarding health and health management. Social capital, as defined in terms of social networks and resources, has a positive impact on a number of areas, notably the health, well-being, and social and economic development of communities. It is also a useful tool for implementing social policy, especially for marginal populations, the elderly, social assistance payments, etc. An action strategy based on the support and development of networks is the key to achieving the social development, health, and well-being of populations. The social ties promoted by these networks provide people with social, cognitive, and emotional support. This has a direct impact on their self-esteem and sense of personal achievement. They also facilitate access to social resources, including social advancement opportunities. In this paper, we examine the vitality, determinants of health, and health management of Canada's minority Francophone 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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.285
Teacher spread0.260 · 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

Citations27
Published2006
Admission routes2
Has abstractyes

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