Needs of Families with Children Who Have a Physical Disability: A Literature Review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".