‘Jumping through hoops’: parents' experiences with seeking respite care for children with special needs
Bibliographic record
Abstract
BACKGROUND: Respite care may act as a means to reduce stress and fatigue in people caring for a dependent who has a disability. Despite this, a variety of barriers may exist to obtaining such services. This study explored caregivers' experiences seeking respite care for their children with special needs within a province in Canada. METHODS: Caregivers were recruited from two agencies providing respite care for children with fetal alcohol spectrum disorders and other mental health and developmental difficulties. In total, 10 caregivers participated in in-depth individual interviews. A constructivist grounded theory approach was employed in the design and analysis of the data. RESULTS: Caregivers discussed their frustrations with the process of finding and obtaining respite care, a course of action described as 'jumping through hoops'. This construct was composed of subcategories emphasizing the complexity of 'navigating the system', the bidirectional process of 'meeting the requirements' and the challenges of 'getting help'. CONCLUSIONS: The collective experiences of these caregivers point to the need for more flexibility and co-ordination of respite care services for children with special 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".