Association Between Therapeutic Interventions and Quality of Life in People With Autism
Bibliographic record
Abstract
Research exploring the association of autism interventions with the quality of life (QoL) of adults with autism spectrum disorders was scarce. Although a multitude of interventions are used to target a specific challenge facing the individual with autism, their correlation with achieving a better QoL is largely unknown. We conducted a cross-sectional, correlational survey study to determine the association between seven interventions—behavioral, social, mental health, daily living skills (DLS), vocational, mindfulness, and medications—and the QoL of adults 18 years and older with autism with no intellectual disability (ID) living in Canada. A national sample of 182 autistic adults or proxy reports completed the survey that used the WHOQOL-BREF to measure subjective QoL. Behavioral, mental health, and medications were the most frequently used interventions (67%, 71.4%, and 82.4%, respectively). QoL was lower across all domains of the WHOQOL-BREF compared with the general population. Hierarchical multiple regression analysis showed that characteristics, such as autism severity, being female, and older age negatively predicted QoL across all domains except for the physical domain, whereas being in a relationship positively predicted social QoL explaining 35.2% of the variance. Of the seven interventions used, behavioral therapies and receiving mental health support consistently predicted a better QoL across all domains, except for the environment domain where only mental health support was a significant predictor. Our findings suggest prioritizing provision of behavioral and mental health interventions to adults with autism and inform future research to evaluate their effectiveness in QoL outcomes as an end goal.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".