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Record W4366446582 · doi:10.3138/cjpe.023.005

Comparaison de trois stratégies de travail en réseau afin de favoriser l’application des connaissances issues de la recherche

2008· article· en· W4366446582 on OpenAlexaffvenue
Marc Daigle, François Chagnon, Danielle Saint-Laurent, Janie Houle

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2008
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIntervention (counseling)Promotion (chess)Public relationsNursingPsychologyPolitical scienceSociologyMedicinePolitics

Abstract

fetched live from OpenAlex

Abstract: In research as in practice, establishing strong teams is a recognized necessity and teams are increasingly large and dispersed. In the field of health care, the need for pluridisciplinarity is a further constraint in the organization of such teams or at least communication within them. Despite these difficulties, networking has emerged among groups of practitioners who share common objectives and try to fulfill their mission more effectively by developing, sharing, and applying knowledge targeted based on their fields of interest. This article is a critical analysis of three different networking experiences that all shared the common objective of application of knowledge through networking among practitioners: the suicide prevention practice community (CoP) of the Centre for Research and Intervention on Suicide and Euthanasia (CRISE), the task force on suicide in prisons of the International Association for Suicide Prevention (IASP), and the international Francophone network for safety promotion and trauma prevention.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.187
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.759
GPT teacher head0.587
Teacher spread0.172 · 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.

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

Citations0
Published2008
Admission routes2
Has abstractyes

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