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Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueQueen Margaret University Publications Repository (Queen Margaret University) · 2020
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsReferralAdverse Childhood ExperiencesPsychological resilienceQualitative researchIntervention (counseling)PopulationSocial supportMental health
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.240
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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