The link between adverse childhood experiences, emotional intelligence and alexithymia : a comparative study between a sample of offenders and the general population
Notice bibliographique
Résumé
Background: Individuals with a history of adverse childhood experiences (ACEs) are likely to display alexithymia and are more prone to engage in criminal behaviors, Emotional intelligence (EI) also plays a significant role in aggression and criminal behavior. Objectives: This study intends to a) assess the relationship between ACEs and alexithymia; b) assess the relationship between ACEs and EI; c) to compare a sample of offenders with a sample of the general population in the variables under study. Methods: The sample comprised a general population and an offender population. This research included a sample of 245 individuals from the general and offender population, with ages ranging from 18 to 68. The participants responded to the Adverse Childhood Experiences Questionnaire (ACEs; Felitti, 1998; Portuguese version, Pinto et al., 2014), that examines various adverse experiences during childhood; the Toronto Alexithymia Scale (TAS-20; Taylor et al.,1992; Portuguese version, Praceres et al., 2000), which assesses characteristics of alexithymia; and the Wong Law Emotional Intelligence Scale (WLEIS; Wong & Law, 2002), which measures emotional intelligence. Results: In the first study, it was observed the offender population revealed higher scores of ACEs and TAS than the general population. In the general population were found statistically significant positive correlations between difficulty identifying feelings, emotional neglect and ACE; statistically significant positive correlations between difficulty describing feelings, and emotional neglect; statistically significant positive correlations between externally oriented thinking, emotional abuse, emotional neglect, and parental divorce; statistically significant positive correlations between emotional neglect and TAS. Within the offender population they were found statistically significant positive correlations between difficulty identifying feelings and emotional neglect; statistically significant positive correlations between difficulty identifying feelings, violence exposure, incarcerated family member and ACE; statistically significant positive correlations between alexithymia and emotional neglect. The findings indicated that individuals within the offender population obtained significantly higher scores of ACEs compared to the general population sample, as well as the total score of ACEs. Additionally, the results revealed that the offender population presents higher scores regarding alexithymia, particularly, in the externally oriented thinking dimension. In the second study, the offender population exhibited higher scores of ACEs and EI when compared to the general population. In the general population it was revealed statistically significant negative correlations between self-emotions appraisal and emotional neglect; statistically significant negative correlations between emotional abuse, physical abuse, sexual abuse, emotional neglect, and regulation of emotions; a statistically significant negative correlations were also found between physical abuse, emotional neglect and WLEIS. In the offender population it was found a statistically significant positive correlations between others’ emotions appraisal and emotional neglect. Conclusion: The findings empathize the importance of developing prevention strategies, to reduce the prevalence of ACEs in the population, both inmate and general population. Further research needs to be conducted including a wider homogeneous sample, to better understand the intricate interactions between ACEs, alexithymia, emotional intelligence and subsequent criminal behavior.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».