Effective Life Management in Parents of Children with Disabilities: A Cross-National Extension
Bibliographic record
Abstract
This study is part of an on-going research program exploring life management in families of children with a variety of disability characteristics and age ranges. Scorgie, Wilgosh, and McDonald (1996) used a qualitative, interview methodology to identify effective strategies, qualities, and transformational outcomes for parents of children with disabilities who had been identified, by service agencies, as having effective life management strategies. A larger group of parents, from similarly identified, effectively managing families, was surveyed (Scorgie, Wilgosh, & McDonald, 1997), using the Life Management Survey (LMS) developed from the nine themes found in the qualitative study, supporting the original findings. A replication of the LMS survey study (Wilgosh, Scorgie, & Fleming, 2000) confirmed the previous findings with parents who were not preselected as effective life managers. In fact, the consistency across the three Canadian studies supported examination of family life management cross-nationally. The present study shows that Catholic Italian parents of children with disabilities have patterns of effective life management strategies, parent qualities, and parent transformational outcomes which characterize them and are similar to those of the Canadian parents. However, in the Italian study, some differences were found related to type of disability, emphasizing the need for qualified professional support, guidance and counselling, focused on the unique needs of each family, as related to the child’s disability needs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".