Virtual group-based mindfulness program for autistic women: A feasibility study
Bibliographic record
Abstract
BACKGROUND: Autistic women experience life differently than autistic men. For example, autistic women tend to be diagnosed significantly later than autistic men, they experience a higher number of traumas, and are at increased risk for mental health conditions. Given gender-specific life experiences, autistic women may benefit from gender-specific group-based supports. Virtual mindfulness has been shown to be helpful in improving well-being among autistic adults; however, limited research has explored the impact of virtual mindfulness when it is delivered to a group of autistic women only. OBJECTIVES: The aim of this article is to describe a preliminary evaluation of a virtual mindfulness group piloted for autistic women. Five key areas of feasibility were assessed in the current study: demand, implementation, acceptability, practicality, and limited efficacy testing. METHODS: Twenty-eight women participated in a 6-week virtual autism-informed mindfulness program and were asked to complete measures assessing psychological distress, self-compassion, and mindfulness at pre and post. Participants were also asked to complete a satisfaction survey after the program. RESULTS: Results showed that the program was feasible in terms of demand, implementation, practicality, and acceptability. While quantitative results showed there were no changes in psychological distress, self-compassion, and mindfulness from pre- to post-program, qualitative results showed some benefits. CONCLUSION: Given the unique challenges that some autistic women experience, offering groups to autistic women may have some value and it would be important to continue exploring this topic area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".