Positive Childhood Experiences and Socio-Economic Association
Notice bibliographique
Résumé
Positive Childhood Experiences are protective social determinants of health factors that mitigate downstream consequences of exposure to chronic adversity and abuse specific to Adverse Childhood Experiences (ACEs). ACEs are defined by the CDC as potentially traumatic experiences during childhood that increase mal-adaptive and risky behaviors, negative health outcomes, and relative health care utilization throughout the lifecycle. This study examines the association between PCE scores and important socio-economic measures. A cross-sectional study using the 2021 Tennessee Behavioral Risk Factor Surveillance Survey PCE module was conducted. The PCE’s module included 7 questions to assess PCE’s during childhood. The 7 PCE questions were consolidated into a measure categorized by low: 0-1 PCE, middle: 2-4 PCEs, and high: 5-7 PCEs. The association between PCE’s and three independent variables of interest were examined: income, employment status, and education. Income was defined by household income and categorized into high ($100,000+) middle ($50,000-$99,000), and low income ($0-$49,000). Education was categorized into high (graduated college or technical school), middle (graduated high school/attended college or technical school), and low education levels (did not graduate high school). Employment status was dichotomized into employed vs unemployed. Chi-square test of independence was used to investigate associations between PCE’s scores and the 3 socio-economic variables, income, employment status, and education, independently. There was a significant association between PCE score and income (P=.001). Over a quarter of individuals with a high PCE score (27%) were in the lowest income category, while 53% were in the highest income category. Of the individuals with a low PCE score 49% were in the lowest income category while 31% were in the highest income category. For individuals with a middle PCE score, 39% were in the lowest income category, while 42% were in the highest income category. The association between PCE score and education level was also significant (P=.001). Of individuals with a low PCE score 61% were employed, those with a middle PCE score 74% were employed, and of samples with a high PCE score 88% were employed. Analysis of PCE score and employment status was also significant (P=.001). Of the individuals with a low PCE score 21% were in the highest education category, of individuals with a middle PCE score 31% were in the highest education category, and of individuals with a high PCE score 41% were in the highest education category. Evidence continues to mount that PCE’s are associated with improved mental health, better social skills, and overall self-reported quality of life on an individual and population level. The findings of this study suggest that positive childhood experiences impact individuals’ ability to overcome adversity if income, education, and employment levels are accepted as proxy measure for quality of life and highlight the importance of fostering positive environments for children to prevent long term negative health, social, and economic impacts. Future research could further explore the mechanisms through which positive childhood experiences lead to positive outcomes, and the implications for interventions aimed at promoting positive childhood experiences across socio-demographic categories.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,005 | 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 ».