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Needs of Families with Children Who Have a Physical Disability: A Literature Review

2012· review· en· W2086308929 on OpenAlexaff
R. C. Siebes, Marjolijn Ketelaar, Jan Willem Gorter, Mattijs W. Alsem, Marian J. Jongmans

Bibliographic record

VenueCritical Reviews in Physical and Rehabilitation Medicine · 2012
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVariety (cybernetics)PsycINFOSpecial needsMedicineResource (disambiguation)Needs assessmentInformation needsSystematic reviewInclusion (mineral)MEDLINEMedical educationPsychologyPsychiatrySocial psychologyComputer scienceWorld Wide WebSociologySocial science

Abstract

fetched live from OpenAlex

Families of children with a physical disability experience a large variety of needs over and above those of families with typically developing children. This study reports the results of a review of the published literature on family needs. A systematic search was conducted in the ERIC, PsycINFO, and PubMed databases from their date of release until December 2011. In addition, reference lists of the assembled articles were examined for relevant literature. Articles were evaluated on study characteristics and family needs; 1823 unique abstracts were screened for eligibility, revealing 69 potentially relevant articles, of which 64 could be retrieved. Analyses of a final selection of 29 articles resulted in a list of 604 family needs (range of 1−36 needs per article), which were clustered and rephrased into 99 unique needs and organized into 14 domains. The results of the review emphasize the variety of needs that these families experience, with "information needs" taking an important place. Our findings may serve as a resource for researchers who are working to develop valid tools to assess needs of families with children with disabilities, and such a tool can be instrumental in tuning the care processes to the individual needs of these families.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.002
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.052
GPT teacher head0.443
Teacher spread0.391 · 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 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

Citations21
Published2012
Admission routes1
Has abstractyes

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