Notice bibliographique
Résumé
Introduction: Animal models have been used in many areas of research to provide insights into mechanisms and treatments for various disorders and diseases. For example, animals are often used in other areas of psychology, such as learning, with examples such as Pavlov’s dogs and Skinner’s rats. Further, animals have also been noted to exhibit psychiatric disorders that are frequently observed in humans, such as depression and anxiety. However, the use of animal models in other less studied fields of psychiatric research is unclear. This poses the questions: is the use of animals effective in studies of common mental health disorders? If so, what aspects of common mental health disorders do current studies focus on? Further, can disorders that have lower prevalence rates also be studied with the use of animals? This paper reviews the use of animals in the study of obsessive-compulsive related disorders of addiction, eating disorders, and trichotillomania (a disorder of compulsive hair-pulling) to answer these questions. Methods: Addiction, eating disorders, and trichotillomania were examined based on ease of study in non-human animals, and sufficient available literature. Nine articles for each disorder were examined to determine types of animals used, and the purpose of animal models in the study. Results: Research shows animal models are often used to study the etiology, genetics, mechanisms, and neurochemistry of psychiatric disorders. Animal models have high validity and translate well to humans. However, treatments of psychiatric disorders are less studied using animal models. Discussion: The review of the current literature suggests animal models are effective in studies of addiction, eating disorders, and trichotillomania. Animal models can be developed to inform various aspects of psychiatric disorders and should be expanded to include studies examining treatments as well. Further, food addiction also should be further assessed using animal models. Conclusion: Overall, animal models are useful in studying various aspects of psychiatric disorders and should continue to be used for those less commonly studied. Future studies with animal models should focus on psychiatric disorders that involve compulsive, repetitive behaviours.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,003 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».