The Safe Environment for Every Kid Model: Analyzing Demographics Related to Higher Risks of Child Maltreatment in High-risk Pediatric Population
Notice bibliographique
Résumé
Background: The Safe Environment for Every Kid (SEEK) screener was developed by Dr. Howard Dubowitz for primary care clinicians to identify and address risk factors for child maltreatment. The instrument queries parents/guardians regarding their families' needs (e.g. food insecurity) and screens for depressive symptoms in the parent/guardian. Objective: To examine the frequency of social determinants of health (e.g., food insecurity) and its correlates using the SEEK screener during pediatric visits at a Federally Qualified Health Center in south central Ohio. Design/Methods: The SEEK screener was distributed to parents/guardians (PG) at Rocking Horse Community Health Center, a Federally Qualified Health Center in Springfield, Ohio, from April 2016 through May 2019. Inclusion criteria were English speaking PG who brought index children (age <19 years) in for an appointment with one of the investigators (JP). Many of the index children had learning or behavior challenges identified by the PG during the visit. 265 PG of the index children completed the survey. Comparisons between demographic characteristics and SEEK variables were made with chi-square tests. Results: The response rate was over 95%; 20 surveys were excluded for this analysis due to incomplete data. The median age of index children was 6 years, 68.1% were white, 86.5% had public health insurance and 52.7% lived with either their mother or father as a single parent. One third of respondents reported annual household income under $10,000, almost one third reported food insecurity, and over one quarter (27.5%) reported feeling down or depressed in the past month. PG of younger children (<6 years) were more likely to report depressive symptoms compared to PG of older children (34.1% vs 22.7%, p=0.048). In contrast, food insecurity was stable across all ages at about one third of the sample. Both depressive symptoms and food insecurity had a strong relation to type of health insurance: 9.4% of PG with private health insurance had depressive symptoms compared to 31.6% with public insurance (p=0.010), and depressive symptoms were reported by 12.1% with private insurance vs. 36.2% with public insurance (p=0.006). Conclusion(s): The SEEK screener is a very useful tool to identify important factors that affect children's well-being, their families' unmet social needs and PG depressive symptoms. While adult depressive symptoms are more common in PG of younger children, food insecurity impacts low income families across their life cycle.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».