Cancer in female firefighters: The clinicobiological, psychological, and social perspectives
Notice bibliographique
Résumé
Objectives: This study explored how demographic characteristics, life experiences, and firefighting exposures have an impact on cancer among female firefighters, and described the types and biologic characteristics of cancers as reported by women in the fire service. Methods: The online survey was available from June 2019 to July 2020. Questions related to demographic characteristics, lifestyle factors, firefighting exposures, and cancer diagnoses. Descriptive analyses characterized variables by the presence or absence of cancer. Qualitative data provided insight into both firefighting and cancer experiences among women. Results: There were 1,344 female firefighter respondents from 12 different countries, 256 of whom provided information on their cancer diagnosis. North American respondents made up 92% of the total. Those with cancer were older, had been in the fire service longer, had more career fires and toxic exposures, and were less likely to still be in active service. They also reported more tobacco use, and more full-term pregnancies. There were no differences in family history of cancer between the two groups. The average age at diagnosis was 39.0 years. The major types of cancer reported included breast (25.4%), cervical (21.1%), melanoma (20.7%), base cell/skin (16.4%), and uterine (14.8%). The cancer was detected when seeking medical attention for symptoms (42.1%), during routine health screening (29.8%), and during specific cancer screening (28.1%). The stage of cancer was reported by 44.5%, and 30.9% included the histopathological grade. Treatments included surgery (72.7%), chemotherapy (14.8%), radiotherapy (13.7%), and observation (13.7%). Challenges associated with cancer included psychosocial (33.2%), financial (18.8%), physical (6.6%), and spiritual (6.3%). Concerns about reporting a cancer experience to their employer included the desire to keep health information private (11.3%), a feeling of vulnerability (7.4%), and being perceived as weak (7.0%). Lack of support from their employer or insurer was also noted. Conclusion: Female firefighters experienced a wide variety of different types of cancers which may come earlier than similar cancers in the public. These findings can help inform resource allocation, the development of new policies, and the need for broader presumptive coverage to support female firefighters diagnosed with cancer.
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 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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».