Activation comportementale et dépression : une approche de traitement contextuelle
Notice bibliographique
Résumé
Depression is a widespread psychological disorder that affects approximately one in five North American. Typical reactions to depression include inactivity, isolation, and rumination. Several treatments and psychological interventions have emerged to address this problematic. Cognitive behavioural therapies have received increasingly large amounts of empirical support. A sub-component of cognitive behavioural therapy, behavioural activation, has been shown to in itself effectively treat symptoms of depression. This intervention involves efforts to re-activate the depressed client by having them engage in pleasant, gratifying, leisure, social, or physical activities, thereby counteracting the tendency to be inactive and to isolate oneself. Clients are guided through the process of establishing a list of potentially rewarding social, leisure, mastery-oriented or physical activities, to then establish a gradual hierarchy of objectives to be accomplished over the span of several weeks. Concrete action plans are devised, and solutions to potential obstacles are elaborated. The client is the asked to execute the targeted objective and to record their mood prior to and following the activity. Behavioural activation effectively reverses the downward spiral to depression. Interestingly, studies show that behavioural activation has a positive effect on cognitive activities. It has been shown to reduce rumination and favour cognitive restructuring, without requiring cognitively-based interventions. The advantage of this treatment is therefore that it is simpler to administer in comparison to full-packaged cognitive behavioural therapies, it requires a lesser number of sessions and can be disseminated in a low-intensity format. This article begins by summarizing the origins of the behavioural model of depression, which serves as a basis to the understanding of behavioural activation. This is followed by a detailed explanation of the different phases involved in a behavioural activation intervention. Empirical support for behavioural activation is then presented in regards to depression as well as comorbid physical and psychological health problems. The results of meta-analyses and randomized controlled trials are presented. Behavioural activation is then discussed within the framework of third-wave therapies, discussing the potential role of mindfulness in behavioural activation objectives. Specifically, it is suggested that mindfulness, although not necessarily directly addressed in behavioural activation interventions, is an integral part of this intervention as clients are asked to record their mood and activities and to become cognizant of the relationship between their symptoms of depression and the participation in activities that provide positive reinforcement. This favours self-awareness and allows clients to realize the impact of their actions on their physical and psychological states. In engaging in self-observation and self-recording, and in participating in a variety of tasks and activities, clients are indirectly encouraged to focus on the here and now rather that to succumb to the depressive tendency that is to ruminate. Suggestions are made as to how therapists can include mindfulness-based activities in the behavioural activation hierarchy. It is hypothesized that, due to the calming effect of mindfulness practices on the nervous system, incorporating mindfulness-based activities-such as yoga, tai chi, Qi Gong, or meditation-could for some people enhance the efficacy of behavioural activation interventions and foster a greater sense of well-being. The article concludes by discussing issues that should be addressed in future research. It is suggested that future studies on behavioural activation explore the impacts of incorporating mindfulness-based activities in the behavioural activation hierarchy in comparison to a traditional hierarchy limited to the accomplishment of gratifying or mastery-oriented tasks, social outings, leisure activities and physical activity.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».