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Record W3111166444 · doi:10.24095/hpcdp.40.11/12.03f

Consommation de substances et méfaits connexes dans le contexte de la COVID-19 : un modèle conceptuel

2020· article· fr· W3111166444 on OpenAlexaffvenue
Aganeta Enns, Adena Pinto, Jeyasakthi Venugopal, Vera Grywacheski, Mihaela Gheorghe, Tanya Kakkar, Noushon Farmanara, Bhumika Deb, Amy Noon, Heather Orpana

Bibliographic record

VenuePromotion de la santé et prévention des maladies chroniques au Canada · 2020
Typearticle
Languagefr
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsPublic Health OntarioUniversity of TorontoOttawa Public HealthUniversity of OttawaPublic Health Agency of Canada
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

À mesure que les effets de la COVID-19 se sont manifestés, une attention croissante a été accordée à la relation entre la COVID-19 et la consommation de substances et les méfaits connexes. Cependant, il existe peu de théories et de preuves empiriques pour orienter les travaux de recherche dans ce domaine. Pour faire progresser ce nouveau champ de recherche, nous présentons un modèle conceptuel qui résume les données probantes, l’information et les connaissances sur la consommation de substances et les méfaits connexes dans le contexte de la pandémie. Le modèle conceptuel offre une représentation visuelle des liens entre la pandémie et la consommation de substances et les méfaits connexes. Il peut être utilisé pour déterminer les futurs domaines de recherche.

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)0.005
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0020.008
Scholarly communication0.0060.006
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.046
GPT teacher head0.377
Teacher spread0.331 · 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 designTheoretical or conceptual
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
Published2020
Admission routes2
Has abstractyes

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