Person-oriented ethics for autism research: Creating best practices through engagement with autism and autistic communities
Bibliographic record
Abstract
Research ethics is an important part of any study. Ethics goes beyond ethics committee approval and consent documents. It addresses broader issues of respect, inclusion, and empowerment in the everyday context of research. This article focuses on everyday aspects of research ethics for studies involving autistic participants. It draws on a review of the literature and a process of ethical deliberation involving a task force of researchers, professionals, autistic self-advocates, and parents. These methods led to the creation of suggestions for researchers. This article describes the community engagement process, briefly presents the task force suggestions, and provides more detailed discussion of select items for illustration. Suggestions are organized around five “person-oriented research ethics” guideposts: (1) individualization (e.g. providing individualized support for participants), (2) acknowledgment of lived world (e.g. acknowledging barriers to care that impact research ethics), (3) empowerment in decision-making (e.g. creating accessible consent processes that address specific communication needs), (4) respect for holistic personhood (e.g. addressing sensory and processing needs and strengths), and (5) focus on researcher–participant relationships (e.g. involving autistic people in ways other than research participants, including but not limited to via participatory research). This project highlights the value of researcher–community partnerships in discussions about research ethics. Lay abstract Research ethics means issues that concern the welfare and wellbeing of people who take part in research. It is important in all scientific studies. Ethics helps people who do research treat people who take part in research fairly and with respect. This article is about day-to-day ethics when autistic people take part in research. We present tips for researchers who want to do this type of study. We used two methods to create these tips. First, we wanted to know what other people said about this topic. We used a literature review to find out. Second, we wanted to know what autistic people, parents, and professionals thought, and had a working group meet to discuss it. The working group provided advice that researchers could consider around day-to-day ethics in research. This article talks about these methods and advice. The advice fits into five big groups: Tailor the research process for the unique needs of each person. Think about the world in which people who take part in research live. Make it easier for people to make their own choices. Value what people who take part in research have to share and consider their needs and strengths. Think about how researchers and people who take part in research work together. This project shows why it is useful for researchers and communities to talk about research ethics together.
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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 | 0.361 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.029 | 0.080 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.007 | 0.056 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".