Beaten but not down! Exploring resilience among female sex workers (FSWs) in Nairobi, Kenya
Notice bibliographique
Résumé
In Kenya sex work is illegal and those engaged in the trade are stigmatized and marginalized. We explored how female sex workers in Nairobi, Kenya, utilize different resources to navigate the negative consequences of the work they do.Qualitative data were collected in October 2019 from 40 FSWs who were randomly sampled from 1003 women enrolled in the Maisha Fiti study, a 3-year longitudinal mixed-methods study exploring the relationship between HIV risk and violence and mental health. All interviews were audio-recorded, transcribed and translated. Data were thematically coded and analyzed using Nvivo 12.Participants' age range was 18-45 years. Before entry into sex work, all but one had at least one child. Providing for the children was expressed as the main reason the women joined sex work. All the women grew up in adverse circumstances such as poor financial backgrounds and some reported sexual and physical abuse as children. They also continued to experience adversity in their adulthood including intimate partner violence as well as violence at the workplace. All the participants were noted to have utilised the resources they have to build resilience and cope with these adversities while remaining hopeful for the future. Motherhood was mentioned by most as the reason they have remained resilient. Coming together in groups and engaging with HIV prevention and treatment services were noted as important factors too in building resilience.Despite the adverse experiences throughout the lives of FSWs, resilience was a key theme that emerged from this study. A holistic approach is needed in addressing the health needs of female sex workers. Encouraging FSWs to come together and advocating together for their needs is a key resource from which resilience and forbearance can grow. Upstream prevention through strengthening of education systems and supporting girls to stay in school and complete their secondary and/or tertiary education would help them gain training and skills, providing them with options for income generation during their adult lives.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,005 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».