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Record W2167536919 · doi:10.1093/deafed/ent041

Social Support for Parents of Deaf Children: Moving Toward Contextualized Understanding

2013· review· en· W2167536919 on OpenAlexaff
Brenda T. Poon, Anat Zaidman‐Zait

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2013
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsLearning PartnershipUniversity of British Columbia
Fundersnot available
KeywordsPsycINFOPsychologySocial supportContext (archaeology)Psychological interventionInclusion (mineral)Hearing lossDevelopmental psychologyService (business)Applied psychologySocial psychologyMEDLINELinguistics

Abstract

fetched live from OpenAlex

In light of claims that social support needs to be defined within specific context, we conducted a metasynthesis to identify ways that social support has been studied and contextualized in research focused on hearing parents of children with hearing loss. A literature search of published articles was conducted to identify research studies related to support and hearing parents of children with hearing loss. Our search yielded 108 items from Psycinfo and 154 items from Web of Science; 26 studies met our inclusion criteria. Our analyses involved summary and integration of information regarding research methods, guiding theoretical frameworks, and findings in relation to diverse support contexts. We found that it is the multidimensionality of social support and its specific functions, individually and in combination, that are particularly relevant in informing family-centered service provision. Further understanding of the multidimensional and dynamic nature of support could occur through investigation of diverse functions of support within and across ecological contexts. Findings from this study contributed to a descriptive framework that can be used to explore the multidimensionality of support; facilitate use of methods that assess specific support functions; and also inform the development of interventions that are responsive and match the needs of parents.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.862
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.374
GPT teacher head0.518
Teacher spread0.145 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations39
Published2013
Admission routes1
Has abstractyes

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