Military Pedagogy and Psychology under the Conditions of the russian-Ukrainian War: Problems, Solutions and Prospects for Development
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Résumé
The article is devoted to the research of the main challenges in military pedagogy and psychology in the context of the russian-Ukrainian war, which has been ongoing since 2014 and escalated in 2022, with an emphasis on foresight strategies for addressing them. The research focuses on three main areas: psychological support for the Armed Forces of Ukraine, preparing citizens for national resistance, and the social and psychological reintegration of veterans. The aim of the research is to develop a scientifically grounded foresight strategy for the advancement of military pedagogy and psychology under the conditions of the russian-Ukrainian war. This includes identifying key challenges and gaps in the system of training military educators and psychologists, justifying the need to establish an institutional framework for coordinating scientific, educational, and practical initiatives, and preparing recommendations for the establishment of a specialised department and research institution on military pedagogy and psychology within the structure of the National Academy of Educational Sciences of Ukraine.The methodological foundation includes an interdisciplinary approach, foresight methodology, content analysis of publications, as well as structural and functional and comparative analysis of the experience from NATO countries (the United States, Israel, Finland, Canada, Australia). The analysis demonstrates the effectiveness of integrating psychological and pedagogical technologies into national security systems. It is proposed to establish a Department and an Institute of Military Pedagogy and Psychology within the National Academy of Educational Sciences of Ukraine to coordinate research, develop the “Military Psychologist” professional standard, educational programmes, and rehabilitation technologies. The foresight analysis outlines baseline, optimistic, and crisis scenarios through to 2030, with the optimistic forecast anticipating a 20% reduction in cases of absence without leave (AWOL), high-quality student training, and a 15% decrease in requests for assistance.The authors highlight systemic shortcomings such as the high rate of absence without leave (80,000 cases in 2024), insufficient psychological training of personnel, and limited stress management competencies among commanders. It is noted that the basic general military training for 70,000 students, introduced under the Law No. 3633-IX from 2025, is hindered by a shortage of personnel and methodologies. The rehabilitation of 1.2 million veterans, including 500,000 combatants, is slowed by the lack of comprehensive programmes, as evidenced by 18,000 requests for psychological assistance in April 2024.The recommendations imply introducing adaptive educational programmes for basic general military training and involving international experts to adapt best practices in the sphere of stress management, resistance training, and veteran rehabilitation.
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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 | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,007 | 0,017 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découle