Présentation et validation de l'Indicateur pédiatrique du Style d'Attachement (PASI)

Favez, Nicolas; Berger, Susan

doi:10.3917/dev.123.0215

Cited by 2 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the scientific literature, after a systematic search in the WoS and Scopus databases, no proposals were found for ML models applied to subjective tools for the analysis of emotions. These tools have been used for research purposes in several studies, to corroborate quantitative results from a qualitative context sustained in observation; Thus, in [9] instruments for direct observation of the emotional behavior of the young child and the mother are applied to validate the Pediatric Attachment Style Indicator (PASI); in [10] a methodology is exposed that complements the subjective evaluation of emotions EMODIANA [6] with sustained evaluation in software. In [11] he develops an observation tool to measure the intrapersonal emotions of youth sports coaches, resulting in 12 categories of behavioral content and eight emotion modifiers (Neutral, Happy, Affectionate, Alert, Tense, Anxious, Angry and Disappointed).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for the Prediction of Emotions in a Mobile Application

Torres-Carrión

Vicente-Tene

Jiménez

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Emotional intelligence is a transversal axis in the integral development of the person. As a variable, in the interaction environment, research is carried out in various fields of science, reaching great advances with the contribution of artificial intelligence. The EMODIANA has been designed as a subjective emotion research tool, with an extension in the EmoAppPro mobile app. The objective is to design and put into production a machine learning model for classification and prediction from the target of emotions, to a simplification (positive, neutral and negative) in real time. The experimentation is carried out with biology students (n = 30) of the Technical Private University of Loja, during an evaluation process, capturing all the video interaction. A thesaurus of emotions is constructed, from observation of experts, with time windows (t = 20 s), applying Fleap's Kappa for label validation. The Knowledge Discovery in Databases -KDD methodology is applied in the ML process, obtaining better results with a model based on a decision tree.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Model for the Prediction of Emotions in a Mobile Application

Torres-Carrión

Vicente-Tene

Jiménez

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

Tempérament, attachement et troubles du comportement chez les jeunes enfants : une revue

Urbain-Gauthier¹,

Wendland²

2014

Devenir

View full text Add to dashboard Cite

Les problèmes de comportement externalisés chez les jeunes enfants constituent un motif fréquent de consultation en clinique infantile. Cet article se propose de faire une revue de la littérature consacrée aux troubles précoces du comportement sous l’angle du concept de tempérament et de celui de l’attachement. Différents travaux explorent les liens entre le tempérament de l’enfant, la qualité des relations précoces, les attitudes parentales et les facteurs de vulnérabilité. Ces recherches témoignent de l’intérêt du concept de tempérament dans les pratiques cliniques de prévention et de traitement des problèmes de comportement chez ces jeunes enfants vulnérables.

show abstract

Présentation et validation de l'Indicateur pédiatrique du Style d'Attachement (PASI)

Abstract: Cet article est la traduction et l'adaptation d'une partie d'un article précédemment paru avec la référence suivante : Favez, N. & Berger, S. (2011). Mother-toddler attachment in a paediatric setting: Presentation and validation of the Paediatric Attachment Style Indicator.

Cited by 2 publications

References 35 publications

Machine Learning Model for the Prediction of Emotions in a Mobile Application

Machine Learning Model for the Prediction of Emotions in a Mobile Application

Tempérament, attachement et troubles du comportement chez les jeunes enfants : une revue

Contact Info

Product

Resources

About