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Étude de la consommation de substances psychoactives et de ses facteurs associés : méta-analyse des études en populations étudiantes

2022· dissertation· en· W6981624730 sur OpenAlexaboutno aff

Notice bibliographique

RevueHAL (Le Centre pour la Communication Scientifique Directe) · 2022
Typedissertation
Langueen
DomaineArts and Humanities
ThématiqueIndian and Buddhist Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCannabisPopulationConsumption (sociology)Socioeconomic statusMental healthPublic health
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The study of the consumption of psychoactive substances in the student population is a subject that has been increasingly addressed over the past 20 years, and constitutes a concern given the changes observed in this population of young subjects at risk of long-term consequences. . However, the great heterogeneity of the studies led us to carry out a meta-analysis, with as main objective, the comparison of consumption between health sectors and other university courses, and as secondary objectives, the impact of geographical and temporal factors, as well as the determinants of mental health. The meta-analysis was carried out using 13 databases, allowing the collection of 193 articles, of which 42 met the inclusion criteria, covering the period from 1978 to 2021, from 11 countries and including 117,217 subjects. After extraction and homogenization of the results, the analysis was carried out with the Jamovi software (version 1.6.23), according to the DerSimonian-Laird random model. Different moderators of interests were assessed using meta-regressions when the number of studies was sufficient. One in five students has used a "drug" at least once in their life, with average age and smoking status appearing to be positively associated. A third of the students have used cannabis at least once in their life, the positively associated moderators being the length of the study, the United States - Canada zone and the health sector. Concerning alcohol, 1% of the students had a daily consumption, with moderators positively associated with the Tunisia-India-Lebanon zone, a younger age and the consumption of amphetamines at least once in their life. Consumption in a "binge drinking" mode concerned 21% of students, with moderators positively associated with younger age, smoking status and the health sector of studies. Regarding prescriptionals psychoactive drugs, 13% of students had taken them at least once in their life, with the negatively associated moderators being the male-female ratio, smoking status and ecstasy consumption. However, for all of these results, there was considerable heterogeneity (I2 > 75%). Only 7 studies assessed anxiety and depressive symptoms (HAD scale), more frequently when dealing with health sector's students. Anxiety scores were higher than those of depression, 4 studies including 2 concerning the health sectors indicated abnormal anxiety scores. The 7 studies evaluating the perception of stress (PSS scale) showed higher scores when it came to students from non-health sectors. This work has made it possible to make an inventory of consumption in a student environment, taking into account their evolution and their geographical differences, as well as the determinants in terms of mental health even if the latter have been have been few explored. In accordance with changes in practices, polydrug use is frequent, and smoking status would be a relevant indicator. Health students were distinguished by higher consumption, a higher level of anxiety but a lower perception of stress compared to students in other courses. However, there are biases in this analysis, among others linked to a large number of French studies, and limits linked to the heterogeneity of the indicators used, the quality and the comparability of the studies included. This work opens up prospects aimed in particular at understanding the specificities of the health sector through a study that would provide answers to the questions raised by this meta-analysis, as well as the implementation of identification actions (example of smoking status), prevention and support within the various sectors of study.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,535
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,308
Écart entre enseignants0,271 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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