To raise a child with autism spectrum disorder: A qualitative, comparative study of parental experiences in the United States and Senegal
Bibliographic record
Abstract
Raising a child with Autism Spectrum Disorder (ASD) can often be a difficult and stressful process for families and caregivers. Though research on ASDs in Africa is burgeoning, very little is known about autism in francophone West Africa. Furthermore, no known ASD studies have explored parental experiences in particular from a cross-cultural perspective. This research used Interpretative Phenomenological Analysis to analyze in-depth, semi-structured interviews with seven Senegalese and seven American families to investigate parental experiences within the Senegalese community with further illustration by cross-cultural comparison. Comparative analysis of data across the two countries was undertaken to identify cultural variables previously unreported, especially those that may affect Senegalese family experience. Analysis of interviews revealed thematic differences in social and community support. Although access to effective treatment services was low among Senegalese families compared to the American families, traditional Senegalese household structures and community relations were hypothesized to serve as protective factors against the high social isolation and resulting logistical struggles reported in the U.S. sample. Further targeted research within the Senegalese environment is recommended, particularly to explore social stigma and its possible effects on families with autism, causal beliefs and treatment practices, and parental mental health and wellbeing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".