Assessing Substance Use and Mental Health in Adolescents With Chronic Conditions
Notice bibliographique
Résumé
Abstract BACKGROUND: Mental health disorders and substance use and abuse are significant issues affecting the health of adolescents. While prevalence of these issues have been studied widely in healthy youth, far less is known about these issues in adolescents with chronic disease. This population may experience adverse health effects from potential interactions between prescribed medications and recreational substances, and effects on adherence and response to treatment may be influenced by both mental health issues and substance use. OBJECTIVES: To determine the prevalence of substance use and mental health disorders in adolescentswith chronic conditions who were receiving care at a tertiary care paediatric centre. DESIGN/METHODS: Patients aged 12-18 with a diagnosed chronic illness, requiring ongoing care for greater than 6 months were recruited from outpatient clinics in Rheumatology , Nephrology and Haematology. Data collected included age, gender, diagnosis and duration, current medications, responses to questions drawnfrom the Ontario Student Drug Use Health Survey about alcohol and substanceuse.The GAIN-SS, a validated screening tool that screens for mental health and substance abuse was also administered, minus one questionwhich asks about suicidal thinking as the responses were collected anonymously. Data were analyzed using simple descriptive statistics and chi-square analysis. RESULTS: Data collection is ongoing. For the first 55 patients from who data has been collected, the mean age was15.3 years, with 69% being female, 29% male, and .02% other. Average grade of last completion was 9.2. Patients with SLE comprised 45% of the sample;15% hada diagnosis of Sickle Cell Disease, 13% Thalassemia, 13% chronic kidney disease, and the remaining participants a variety of other rheumatologic and haemato-logic diagnoses. On average, patients were currently taking 2.7 medications. Substance use was infrequent with 70% of participants reported never having drunk alcohol or only trying a sip, and 85% reporting never having tried cannabis. The opposite was true of mental health symptoms, with over 50% endorsing significant low mood overpast year, and a similar proportion endorsing significant problems with anxiety. 13% endorsed missing meals or self inducing vomiting as a way to control their weight. CONCLUSION: There are several possible reasons that this cohort had-lower than expected alcohol and substance use for their age. Their chronic illnessmay limitinteractions with peers,with whom initial teen alcohol and cannabis experimentation tends to occur. They may also have made con-cious decisions not to use because of their illness and treatments. Significantmood and anxiety symptoms that were endorsedwarrant further assessment and may have significant impact on their treatment and overall functioning. The data did not reveal that any of them were receiving phar-macologic treatment for either depression or anxiety. These results suggest that routine screening for mental health symptoms to inform further assessment is warranted in young people with chronic medical conditions.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 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 ».