101 Screening for Psychosocial Risk in Families of Children with Medical Complexity (CMC)
Notice bibliographique
Résumé
Abstract Background Children with medical complexity (CMC) are defined by their medical fragility, dependence on assistive technology and substantial care needs. Family caregivers of CMC have unique challenges, such as prolonged hospitalizations and poor care coordination, which result in extensive caregiver stress. There is a great need to quantify the level of psychosocial stress and resilience in these caregivers to allow for appropriate allocation of health care resources. The Psychosocial Assessment Tool (PAT) is a brief parent-reported screening tool for measuring psychosocial risk in caregivers of pediatric populations. This is the first study to use the PAT in children with medical complexity. Objectives To quantify psychosocial risk in family caregivers of children diagnosed with medical complexity. To identify predictors of caregiver distress based on their sociodemographic factors. It was hypothesized that the extensive health demands of CMC will result in high amounts of chronic, ongoing caregiver distress relative to the previously studied pediatric populations. Design/Methods This cross-sectional study was conducted at The Hospital for Sick Children, Toronto, Canada. Family caregivers of children with medical complexity completed the PAT questionnaires during regularly scheduled Long-Term Ventilation and Complex Care clinic visits. Based on the overall PAT scores, caregivers were stratified as “Universal” low risk (<1.0), “Targeted” intermediate risk (1.0 to 1.9), or “Clinical” high risk (≥2.0). Multiple linear regression analysis was performed to examine the effect of sociodemographic variables and illness severity on total PAT scores. Comparisons with previous pediatric studies were made using T-test statistics. Results 136 [103 females (76%)] family caregivers completed the study. Mean PAT score was 1.17 (SD = 0.740). 61 (44.85%) caregivers were classified as Universal risk, 60 (44.12%) as Targeted risk, and 15 (11.03%) as Clinical risk. Compared to previously studied pediatric populations, our CMC have the second-highest overall PAT scores, which are also substantially weighted towards the higher risk categories (Table 1). Multiple linear regression analysis demonstrated that subjective report of financial hardship by caregivers is a significant predictor of total PAT scores (p < 0.05). Conclusion Family caregivers of children with medical complexity report PAT scores amongst the highest of all pediatric populations. These caregivers experience significant psychosocial distress, demonstrated by larger proportions of caregivers in the Targeted and Clinical risk categories. Therefore, psychosocial interventions including financial assistance are urgently needed in this population.
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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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».