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Record W1978359566 · doi:10.1177/152483902236718

Promoting Physical Activity at the Community Level: Insights into Health Promotion Practice from the Laval Walking Clubs Experience

2002· article· en· W1978359566 on OpenAlexaffabout
Nguyet Minh Nguyen, Lise Gauvin, Irène Martineau, Richard Grignon

Bibliographic record

VenueHealth Promotion Practice · 2002
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité de MontréalCentre Integre de Sante et de Services Sociaux de Laval
Fundersnot available
KeywordsClubHealth promotionIntervention (counseling)Promotion (chess)Active livingPublic healthBest practicePhysical activityPublic relationsGerontologyPolitical scienceMedicineNursingPoliticsPhysical therapy

Abstract

fetched live from OpenAlex

In an effort to develop a model of best practices, we deconstruct a successful initiative to promote physical activity at the community level that originated from a public health directorate located in a suburban city adjacent to a metropolis. The main thrust of the intervention consisted of creating walking clubs throughout the area of the public health directorate to provide an alternative physical activity service to sedentary adults. The deconstruction of the initiative and its associated procedures was developed through an extensive interview of the public health official in charge of the initiative and through an examination of archival materials. We conclude that a model of best practices to promote physical activity at the community level should be ecological and therefore should include intervention components to mobilize the community around physical activity, coordinate existing municipal and community organizations, and provide ongoing support to volunteer club directors.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.011
Scholarly communication0.0090.003
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.287
GPT teacher head0.499
Teacher spread0.211 · 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 designQualitative
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

Citations8
Published2002
Admission routes2
Has abstractyes

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