91 Health care needs and missed care among youth in care in British Columbia: A population study
Notice bibliographique
Résumé
Abstract Primary Subject area Adolescent Medicine Background Youth in care (YIC), including those living in foster care, kinship care, group homes, and youth agreements, are a vulnerable population with many risk factors leading to a high prevalence of mental and physical health needs. YIC are recommended to have more frequent health care encounters than the general adolescent population, though it is unknown how Canadian YIC perceive whether their health care needs are sufficiently met. Objectives To assess YIC's perception of their health care needs and frequency of missed care, defined as not having received needed health care. Design/Methods A representative sample of 38,015 students in Grades 7 to 12 across British Columbia was surveyed in the 2018 BC Adolescent Health Survey (McCreary Centre Society). Questionnaire items on demographics, mental and physical health, and health care access in the past year were examined. Frequencies and cross-tabulations were performed using IBM SPSS® Complex Samples module software. Results In the past year, 1.9% of respondents reported living in government care. YIC had a mean age of 14.76 years and were 50.9% female. YIC reported worse mental health (46.5% vs. 27.6% poor/fair rating, p < 0.01) and physical health (36.4% vs. 19.1% poor/fair rating, p < 0.01) compared to non-YIC, with female and non-binary YIC most severely impacted. YIC were less likely to report not needing health care (15.6% vs. 21.3%, p < 0.01) and more likely to report missed care (11.2% vs. 3.1%, p < 0.01) compared to non-YIC. Although the rate of any health care usage was not significantly different between the groups, nearly one-quarter (23.7%) of YIC accessed health care at 3 or more locations, compared to only 16.4% of non-YIC (p < 0.01), with YIC accessing counsellors/psychologists and youth clinics more frequently. YIC reported more missed mental health care (32.9% vs. 18.4%, p < 0.01) and physical health care (21.6% vs. 7.8%, p < 0.01) than non-YIC, with female YIC reporting more missed care than male YIC. Non-binary YIC also reported more missed mental health care than male YIC. YIC were more likely than non-YIC to have missed mental health care due to reasons such as prior negative experiences and lack of transportation. Conclusion YIC reported worse mental and physical health and greater frequencies of missed care compared to non-YIC, especially female and non-binary YIC. Further attention is needed in addressing systemic and individual barriers to health care in this vulnerable population.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».