“We are exhausted, worn out, and broken”: Understanding the impact of service satisfaction on caregiver well‐being
Bibliographic record
Abstract
Few studies exist that have examined the impact of service-related factors and system-level disruptions (i.e., the pandemic) on families of autistic children in Canada using large sample sizes. To address this gap, the goal of this research was to examine the impact of satisfaction with autism services on caregiver stress, controlling for important demographic variables, such as family income, marital status, and child level of support needs. The impact of navigating and accessing services on parent well-being was also explored. A total of 1810 primary caregivers of autistic children or youth living in Ontario, Canada completed a survey with both closed- and open-ended questions in the summer of 2021. A hierarchical multiple regression was conducted to examine the impact of satisfaction with autism services on caregiver stress. Open-ended responses on the survey from a subset of the sample (n = 637) were coded using thematic analysis to understand the impact of navigating and accessing services on parent well-being. Satisfaction with services significantly predicted caregiver stress after controlling for marital support, family income, and child level of support needs. Qualitative analysis revealed impacts of navigating and accessing services in three areas: (1) Physical, (2) Emotional/Psychological, and (3) Financial Well-being. Understanding parent perceptions of satisfaction with services can shed light on strategies for improving services that support parent well-being.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".