How to support families of children with disabilities? An exploratory study of social support services
Bibliographic record
Abstract
Abstract Support services to families of children with disabilities have previously been documented. While the effectiveness and consequences of some support strategies have been defined, their comparison remains problematic primarily because of the diversified existing definitions. The present study aimed to elaborate and validate a typology to describe different types of support that can be offered to families of children with disabilities. A review of literature highlighted a variety of support services and allowed a categorical grouping. Content analysis ensured that each category was defined distinctively. Afterwards, a panel of experts and representatives of organizations from seven developed countries (Australia, Belgium, Canada, Denmark, France, Sweden and Switzerland) validated the typology. A database of services offered in these countries was created. The resulting typology was divided into four categories related to the family needs: support, respite, child minding and emergency support. Each type of support can be illustrated within organizations in the database. As such, social workers can use the defined typology to identify the needs of families of children with disabilities and suggest alternatives when services are not available. Overall, the described typology should facilitate discussion between stakeholders and families by providing a common communication system.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".