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Enregistrement W6987431142

A Survey of the Practices of Social Workers Working with Children with Adverse Childhood Experiences and Speech, Language, and Communication Needs

2020· dissertation· en· W6987431142 sur OpenAlexaboutno aff

Notice bibliographique

RevueQueen Margaret University Publications Repository (Queen Margaret University) · 2020
Typedissertation
Langueen
DomainePsychology
ThématiqueChild Abuse and Trauma
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReferralAdverse Childhood ExperiencesPsychological resilienceQualitative researchIntervention (counseling)PopulationSocial supportMental health
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

There is evidence to suggest that children who have Adverse Childhood Experiences are at increased risk of having Speech, Language, and Communication Needs, physical health problems, and mental health issues compared to their non-maltreated peers (Law and Conway 1992; Felitti et al. 1998; Trocme et al. 2010; Lum et al. 2015; Sylvestre et al. 2016). Building resilience in children is essential for supporting children's health. Resilience develops through healthy relationships with adults, including parents, teachers, Speech Language Pathologists, and Social Worker (Schore 2003; Ellis and Dietz 2017). Once Social Workers identify children with Adverse Childhood Experiences, they can refer to Speech Language Pathology services. Speech Language Pathologists can provide early intervention for children with Adverse Childhood Experiences and Speech, Language, and Communication Needs to support their speech, language, and communication development. This may benefit other areas of development- cognitive, emotional, and physical as well (Fox and Rutter 2010; Guralnick 2011). This research study explored Social Worker's perspectives and knowledge of Adverse Childhood Experiences and Speech, Language, and Communication Needs in children, referral practices, multidisciplinary teams, and collaborative practices in Newfoundland and Labrador. An online survey was sent to a population of SWs in NL who have experience working with children. Quantitative and Qualitative data was collected and presented in Tables and Figures. Qualitative data were assigned codes and grouped into main themes. The data collected was linked to the research aims of the study. 57 Social Workers living in NL responded to the online survey. Results indicated that the Social Workers’ knowledge base of Adverse Childhood Experiences and Speech, Language, and Communication Needs is high. Respondents understand the impact that Adverse Childhood Experiences can have on a child's speech, language, and communication development. Respondents reported that multidisciplinary teams involving Speech Language Pathologists and Social Workers could benefit the services provided to children in Newfoundland and Labrador and enable better access to early intervention services. Respondents indicated that further learning opportunities' in the area of Adverse Childhood Experiences and Speech, Language, and Communication Needs would benefit their profession. Respondents stated that the current needs of children with Adverse Childhood Experiences and Speech, Language, and Communication Needs are not being met. The need for more referrals to the Speech Language Pathology service is indicated. The first recommendation of the survey findings is that when Social Workers identify children with Adverse Childhood Experiences, a Speech Language Pathologist should screen children for Speech Language, and Communication Needs. The second recommendation is for further learning opportunities involving Social Workers and Speech Language Pathologists to build more awareness of Speech, Language, and Communication Needs. The third recommendation is that Speech Language Pathologists have further access to children and families who have experienced adversity so that specialist Speech Language Pathology intervention can occur to meet each child's communication needs and to engage the family in therapy and goal setting.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,079
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,240
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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