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Record W2981795657 · doi:10.1177/1367493519883456

Family support and family and child-related outcomes among families impacted by disability in low- and middle-income countries: A scoping review

2019· review· en· W2981795657 on OpenAlexaff
Reshma Parvin Nuri, Beata Batorowicz, Heather M. Aldersey

Bibliographic record

VenueJournal of Child Health Care · 2019
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsLow and middle income countriesPsychologyMedicineDevelopmental psychologyEconomic growthDeveloping countryEconomics

Abstract

fetched live from OpenAlex

There is a growing interest in understanding the relationship between family support and family or child-related outcomes in high-income countries. However, this has received little attention in low- and middle-income countries. The purpose of this review was to explore the relationship between family support and family and child-related outcomes among families affected by disability in low- and middle-income countries. We conducted a scoping review of five databases using search terms related to 'family', 'support', 'child', and 'disability'. A total of 13 articles met the inclusion criteria. Families of children with disabilities received most of their support from informal sources (e.g. immediate family members, friends, and parents support groups). Parental stress was most often evaluated as the family outcome and was negatively linked to emotional support and childcare assistance from immediate family members. Movement and mobility therapy offered by rehabilitation professionals was found to improve children's walking patterns. Positive attitudes from community members were key facilitators to participation of children with disabilities in social activities. The review calls for urgent attention to research in low- and middle-income countries, particularly the extent of support families are receiving from government-led support systems.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.043
GPT teacher head0.419
Teacher spread0.376 · 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 designSystematic review
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

Citations38
Published2019
Admission routes1
Has abstractyes

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