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Record W2895960864 · doi:10.1111/cch.12624

Needs of families of children with cerebral palsy in Bangladesh: A qualitative study

2018· article· en· W2895960864 on OpenAlexaff
Reshma Parvin Nuri, Heather M. Aldersey, Setareh Ghahari

Bibliographic record

VenueChild Care Health and Development · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCerebral palsyRehabilitationSpecial needsQualitative researchNeeds assessmentPsychologyMedicinePsychiatryPhysical therapySociology

Abstract

fetched live from OpenAlex

PURPOSE: Families of children with disabilities often have needs related to the care of their child with a disability. Although there has been extensive exploration of family needs in high-income contexts, there is little known about this issue in low and middle-income countries like Bangladesh. In this study, we explored the needs of families of children with cerebral palsy in Bangladesh. Such understanding is important as it will help to improve services for children with disabilities and their families. METHODS: We used a qualitative approach and interviewed 20 family members of children with cerebral palsy who visited the Centre for the Rehabilitation of the Paralysed, Bangladesh. We thematically analyzed data from semistructured interviews. RESULTS: Five different themes were found on needs of families with children with disabilities: (a) financial needs, (b) needs for disability-related services, (c) needs for family and community cohesion, (d) informational needs, and (e) emotional needs. Participants overwhelmingly reported that financial needs were their highest priority. CONCLUSION: Needs of families of children with disabilities must be considered in rehabilitation services to improve children's outcomes. Further studies are required to explore needs of families of children with disabilities who do not have access to rehabilitation services.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.043
GPT teacher head0.388
Teacher spread0.345 · 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 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

Citations39
Published2018
Admission routes1
Has abstractyes

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