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Record W3210626288 · doi:10.1037/cap0000289

Rôle des compétences interpersonnelles sur la détresse des enquêteurs en exploitation sexuelle d’enfants: Réflexion théorique.

2021· article· fr· W3210626288 on OpenAlexaff
Audrey Potz, Annie Gendron, Julie Maheux

Bibliographic record

VenueCanadian Psychology/Psychologie canadienne · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMontreal Police ServiceMichel-SarrazinUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Résumé \nLa nature potentiellement traumatisante du travail des enquêteurs en exploitation sexuelle et physique d’enfants (EESPE) les rend vulnérables à développer des symptômes de détresse psychologique. La plupart des études ont mis l’accent sur l’importance du soutien et de l’utilisation de stratégies d’adaptation positives pour favoriser le bien-être des enquêteurs. Or, certaines compétences interpersonnelles pourraient aussi jouer un rôle protecteur en contextes émotionnellement chargés, comme il a été démontré auprès d’autres types d’intervenants exposés au stress et à des clientèles vulnérables. Parmi ces compétences, la mentalisation, soit la capacité à comprendre ses états mentaux et ceux des autres au sein des relations, pourrait s’avérer centrale pour ces intervenants étant donné ses effets bénéfiques sur le bien-être psychologique. Considérant que les enquêteurs sont constamment en contact avec des récits ou images potentiellement traumatiques, qu’ils interagissent de façon continue avec des individus en détresse et qu’ils sont exposés à différents stresseurs, la mentalisation pourrait les aider à mieux gérer la charge émotionnelle reliée à leur contexte de travail. Cet article propose donc un regard sur les stresseurs spécifiques au travail des EESPE, les impacts sur la détresse psychologique et les facteurs de protection de leur bien-être psychologique. Un modèle théorique sera présenté, proposant la mentalisation comme étant une compétence interpersonnelle pouvant jouer un rôle dans le bien-être des EESPE. Les implications potentielles de la mentalisation pour la formation et la pratique professionnelles seront discutées. \n \nAbstract \nThe potentially traumatizing nature of the work of researchers into sexual and physical exploitation of children (RSPEC) makes them vulnerable to developing symptoms of psychological distress. Most studies have emphasized the importance of support measures and the use of positive adaptation strategies to promote researcher welfare. Certain interpersonal skills may also play a protective role in an emotionally charged context, as has been demonstrated among other types of intervenors, who are exposed to stress and vulnerable patients. Among these skills, mentalization, the ability to understand one’s own mental state and that of others with whom they are relating, could be crucial for these intervenors, given the beneficial effects on psychological well-being. Considering that these researchers are in constant contact with potentially traumatic stories and images, that they continually interact with distressed people, and that they are exposed to different stressors, mentalization could help them to better manage their working contexts. This article proposes an investigation of the stressors that are specific to RSPEC work, their impact on psychological distress and protective factors for psychological welfare. A theoretical model will be presented identifying mentalization as an interpersonal skill that could play a role in RSPEC welfare. Potential implications of mentalization for training and professional practice will be discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.012
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.044
GPT teacher head0.338
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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