A Systematic Review of Military to Civilian Transition: The Role of Gender
Notice bibliographique
Résumé
Abstract Background The military-to-civilian transition can be a challenging period for many service members; however, recent research suggests that female ex-service personnel (veterans) confront additional complexities during reintegration into civilian life. This systematic review aimed to identify and synthesise findings across qualitative studies exploring the impact of gender on this transition process. Methods Peer-reviewed literature was drawn from a multi-database search, limited to qualitative studies. The studies included either female veterans or both male and female veterans aged 18 years or older who had previously served in the Armed Forces within the Five Eyes (FVEY) countries (Australia, Canada, New Zealand, the United Kingdom, and the United States). We used a Framework Analysis approach to guide the synthesis of the qualitative data. An assessment of study quality was conducted using the Joanna Briggs Institute (JBI) Qualitative Critical Appraisal Checklist for Qualitative Studies. The study protocol is registered with the Open Science Framework (osf.io/5stuj). Results In total, 10,113 articles were screened after the removal of duplicates, 161 underwent full-text review, with 19 meeting the eligibility criteria. The review identified eleven themes split across individual’s experience whilst serving and after transitioning out of the military service. Both male and female veterans discussed a period of acculturation when they joined service and adapted to military norms, culture and identity. Female veterans faced additional challenges at this stage centred on the conflict between feminine norms and the military masculine ideal. Upon leaving service both male and female veterans experienced a loss of military identity and purpose, and dissonance with civilian norms illustrating a military-civilian divide. For female veterans, adjustments and adaptations learned in the military clashed with civilian feminine norms and stereotypically male veteran culture. Female veterans also struggled with the legacies of gender inequality, discrimination, and sexual assault which affected their development of a female veteran identity and affected the provision of services designed to meet their needs as a female. Despite these challenges, female veterans’ expressed pride in their service and accomplishments. Conclusions Any effort to improve the military-to-civilian transition should take account of the legacy of gender discrimination, especially within the military service, and the potential mismatch between historical civilian female norms and the more traditionally masculine norms of military life. Disclosures This project was supported by a grant from the Forces in Mind Trust (FiMT) 2202. Full ethical clearance was granted by the Health Faculties Research Ethics Subcommittee, King’s College London. Project Reference: HR/DP-22/23-33303.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,155 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,017 | 0,018 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».