Experiences with unregulated respite care among family caregivers of children dependent on respiratory technologies
Bibliographic record
Abstract
Increasingly, children with respiratory conditions who are dependent on medical technology (e.g. ventilators and tracheostomies) are cared for at home by family caregivers who are at risk for significant health, financial and social burdens. In many jurisdictions, access to quality respite is varied and often the availability of regulated (nursing) providers is insufficient. Rather than go without, some families have secured alternative and unregulated providers to supplement formal home care systems. The purpose of this study was to explore the experiences of family caregivers of children dependent on respiratory technologies who have used unregulated providers for in-home respite care. Through an interpretative description approach, data was gathered from 20 semi-structured parent interviews and analysed using constant comparative analysis. Four themes emerged from the data, which were conceptualized as both opportunities and tensions that parents experienced with both unregulated and regulated home care providers: finding the right fit for the child and family; trusting the provider is everything; using unregulated providers offers unique advantages; and accepting that regulated and unregulated care present challenges. Findings signal that unregulated providers play a pivotal role in supporting parents of children who are dependent on respiratory technologies. Implications for practice, policy and future research initiatives are discussed.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".