Reconditioning Emotional Responses With the Break Method: Pilot Quantitative Study
Notice bibliographique
Résumé
BACKGROUND: The Break Method is a structured, behavior-based emotional reconditioning program designed to help individuals gain insight into patterns of emotional dysregulation and reprogram behavioral responses rooted in past experiences. Although it has been widely adopted in private and small-group settings, empirical evidence supporting its effectiveness remains limited. With increasing interest in accessible, scalable, and personalized mental health interventions, evaluating the outcomes of such programs is essential for informing future implementation and research. OBJECTIVE: This pilot study aimed to evaluate changes in self-reported mental health status before and after participation in the Break Method program. Specifically, we sought to examine (1) overall trends in mental health improvement, (2) associations between specific reasons for joining the program and changes in mental health outcomes, and (3) latent clusters of participant motivations based on symptom profiles. METHODS: Data were collected from 175 unique participants, yielding 195 total survey responses (as 15 participants completed the program more than once). Participants rated their mental health status on a 5-point Likert scale both before and after the program (this was not a validated clinical measure, limiting the interpretability and comparability of results). Descriptive statistics and paired 2-tailed t tests were used to assess pre- and postprogram differences in Likert scores. McNemar tests were conducted to compare categorical mental health status (Likert score ≥4 vs <4) before and after participation. Analyses of covariance examined score changes across groups stratified by reported reasons for joining. Multiple correspondence analysis was used to explore latent symptom clusters. RESULTS: Before program participation, 186 of 195 (95.4%) responses reported Likert scores below 4. Following the program, 157 (80.5%) responses reported scores of 4 or higher. A significant improvement in mental health status was observed (preprogram mean score 2.07 SD 0.82, postprogram mean score 3.92 SD 0.73; P<.001). Significant, positive changes were associated with reasons including anxiety (β=0.332, 95% CI 0.073-0.591), obsessive-compulsive disorder (β=0.455, 95% CI 0.061-0.850), and a history of self-harm or suicidal ideation (β=0.511, 95% CI 0.091-0.931). The multiple correspondence analysis identified three clusters of participants based on symptom profiles: (1) low self-image (eg, depression, self-sabotage, and relationship issues); (2) life-development goals (eg, self-discovery and future planning); and (3) obsessive-compulsive disorder-related symptoms. The first cluster was significantly associated with improved mental health outcomes (β=0.348, 95% CI 0.060-0.636). CONCLUSIONS: The Break Method appears to be a promising intervention for improving mental health, particularly among individuals reporting anxiety, low confidence, or a history of self-sabotage. However, due to the single-group, preprogram-postprogram design without a control group, causality cannot be inferred, and these findings should be interpreted as preliminary associations rather than confirmed efficacy. Future studies should incorporate standardized clinical tools, control groups, and longitudinal designs to validate these results and explore long-term outcomes across diverse populations.
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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».