Integrating Community Participation With Interpretative Phenomenological Analysis: Reflections on Engaging the Autism Community
Bibliographic record
Abstract
Members of the autistic community have long advocated for more input into and participation with autism-related research. Currently, the power to determine the direction of autism-related research and knowledge production related to autism lies with non-autistic researchers, while the wishes and perspectives of the autistic community are largely ignored. There is a growing trend toward ethical autism-related research, however, in which the perspectives of all stakeholders, particularly those of autistic individuals, are sought and their expertise on autism is foregrounded. In a study exploring the experiences of childhood adversity and resilience among autistic adults, we strove to conduct our research in an inclusive and ethical way, by integrating participatory methods, such as community engagement to inform research design, and credibility checking with participants to confirm that the analysis resonated with their experiences. Five stakeholders, representing parents of children on the autism spectrum, professionals, and autistic community members were recruited to provide input into the research design and provide insight into autistic ways of communicating, interacting, and being. The recommendations generated through this community engagement were then integrated into an interpretative phenomenological analysis (IPA) framework and implemented with four adult autistic participants. Through reflection on the process of community engagement, development of research design, implementation of the study, and credibility checking, it is clear that incorporating participatory methods into IPA increases rigor and ensures that autistic perspectives are represented through research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".