La mentalisation comme base théorique de l’intervention assistée par l’animal chez les jeunes témoins ou victimes d’actes criminels
Bibliographic record
Abstract
0 2 3 J U C X A J C | 8 1 L'intervention assistée par l'animal (IAA) est de plus en plus pratiquée chez les jeunes, notamment chez ceux avec des troubles mentaux graves, diagnostiqués d'un trouble du spectre de l'autisme (Hill et al., 2020), atteints du cancer ou victimes d'abus sexuels.L'IAA permet de créer un environnement sécuritaire, empreint de confiance et d'ouverture, ce qui est particulièrement important pour les enfants victimes d'abus sexuels ou souffrant de stress post-traumatique (Dietz et al., 2012).Le présent article propose que les effets bénéfiques de l'IAA puissent être corroborés en partie par le rôle facilitateur des animaux dans le processus de mentalisation, en s'appuyant sur la littérature scientifique.La mentalisation consiste à interpréter et à ressentir ses propres émotions, en plus de prendre en considération celles d'autrui (Bateman et Fonagy, 2012).En ce sens, plusieurs études ont avancé que l'animal pourrait agir d'outil dans l'intervention afin d'aider les enfants à mentaliser non seulement les émotions et les perceptions d'autrui, mais également les leurs (Carlsson et al., 2015;Dietz et al., 2012;Scandurra et al., 2021;Stetina et al., 2011). R É S U M ÉAnimal-assisted intervention (AAI) is increasingly practiced for children, notably in those with severe mental disorders (Stefanini & al., 2015), diagnosed with autism spectrum disorder (Hill & al., 2020) or with cancer (McCullough & al., 2018), and victims of sexual abuse (Krause-Parello & al., 2018).AAI helps create a safe environment of trust and openness, which is particularly important for children who are victims of sexual abuse or suffer from post-traumatic stress (Dietz & al., 2012).This article suggests that the beneficial effects of AAI could be supported in part by the facilitating role of animals in the process of mentalization.Mentalization consists of interpreting and feeling one's own emotions, in addition to taking into consideration those of others (Bateman & Fonagy, 2012).In this sense, several studies have argued that animals could act as a tool to help children mentalize not only the emotions and perceptions of others, but also their own (Carlsson & al., 2015;
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".