Parental experiences in the use of fluoxetine for management of ‘disruptive behaviours’ in children and youth with autism and pathological demand avoidance – a mixed methods exploratory study (Fluoxetine study)
Bibliographic record
Abstract
Abstract Background Pathological demand avoidance (PDA) is a relatively new profile described in autism spectrum disorder (ASD), characterised by an extreme resistance to everyday demands and social interactions and may present as aggressive, destructive behaviours. This study aims to explore (through use of parental recall) the utility of fluoxetine in managing disruptive behaviours attributed to PDA. Methods Mixed-methods study incorporating surveys and semi-structured interviews with caregivers. Results Extreme Demand Avoidance 8-item measure (EDA-8) survey results showed a significant decrease in most behaviours attributed to PDA, including a decrease in frequency of the child obsessively resisting and avoiding ordinary demands and requests and having difficulties complying with demands unless they were carefully presented. The majority of participants in this study indicated that their child had either a co-occurring diagnosis of an anxiety disorder or that their child experienced anxiety. After initiating fluoxetine, parents noted improvement in their child’s behaviours and consequently improvement in their family’s quality of life. Conclusion Fluoxetine shows promise as an effective treatment in improving disruptive behaviours in children with PDA. Given the high prevalence of anxiety in children with PDA, we propose that the disruptive behaviours of PDA are secondary to an embodied experience of anxiety in children with autism.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".