The Walking Interview: A Promising Method for Promoting the Participation of Autistic People in Research Projects
Bibliographic record
Abstract
Walking interviews are increasingly used in the field of health to understand the relationship between individuals and places. With this method, the interviewer and the participant move from place to place within an environment and use it to enrich the discussion. Several advantages have been reported concerning its use, such as the richness of the data it provides and that it allows interviewers to immerse themselves in the participant’s world. This article is based on the experience of using walking interviews in an innovative context, with 10 autistic adolescents and adults and 13 parents. The method was used in the participants’ home environment, as part of study conducted in Québec (Canada) on home environment factors that influence autistic people’s independence at home. It was chosen to meet the study objectives, but also to support the participation of autistic people in research interviews. These people’s participation in research can be a challenge when data collection methods are not adapted, given the difficulties that some have communicating and interacting socially, as well as discussing abstract topics. In this article, the advantages, limitations and suggestions related to the use of walking interviews are reported from the participants’ and interviewer’s perspectives. The authors also discuss the potential for using walking interviews to collect the perspectives of other populations, especially those with difficulties expressing themselves, such as allophones or people with language disorders.
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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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".