Nor weak ties, nor strong ties: Personal support networks and relations between autistic peers
Bibliographic record
Abstract
The aim of this study is to analyze egocentric social networks of 31 autistic adults in Québec (Canada). We examined the composition of their proximal personal support network and the role of support shared among autistic peers. A mixed method analysis was used. An egocentric network questionnaire that included qualitative prompts was conducted to gather participants’ interpretations and reference systems, facilitating an understanding of networks’ multidimensional aspect. A reflexive thematic analysis was conducted with a subset of the data from the qualitative prompts. Three main themes were identified: (1) interference in social interactions, (2) mutual acknowledgment, and (3) social engagement in the community. Most participants ( n = 21) mentioned autistic peers, but although these peers may have provided support, participants rarely identified them as being part of their proximal personal support network. Results nevertheless highlight the importance of peer support for several participants, which can refer to the concept of “weak ties,” but they differ from it by the exchanges of emotional support, which are normally expected from “strong” ties. Lay abstract Social support is recognized as an important predictor of quality of life in autistic and neurodivergent people. However, few studies have explored in detail the composition of support networks in autistic adults. Moreover, research on social networks in the field of autism has mainly focused on the support networks of experts and parents. This article presents the results of a study that analyzed the composition of the support network of 31 autistic adults and examined the role of autistic peer support in their network. Interviews were conducted with the participants. Most of them ( n = 21) mentioned autistic peers in their social network. Although these peers provided unique types of support, participants rarely identified them as close friends or relatives. Nevertheless, the results underline the importance of peer support for participants, which often presented itself as empathetic listening, the sharing of advice to deal with everyday life issues and company for recreational activities. This study shows that we should not underestimate the support that can be provided by people who share a common experience, even if they are not considered close friends. It paves the way to thinking about how communities and professionals, such as social workers and educators, can support opportunities and facilitate spaces that foster peer support.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".