Representativeness of autistic samples in studies recruiting through social media
Bibliographic record
Abstract
Survey-based research with recruitment through online channels is a convenient way to obtain large samples and has recently been increasingly used in autism research. However, sampling from online channels may be associated with a high risk of sampling bias causing findings not to be generalizable to the autism population. Here we examined autism studies that have sampled on social media for markers of sampling bias. Most samples showed one or more indicators of sampling bias, in the form of reversed sex ratio, higher employment rates, higher education level, lower fraction of individuals with intellectual disability, and later age of diagnosis than would be expected when comparing with for example population study results from published research. Findings from many of the included studies are therefore difficult to generalize to the broader autism population. Suggestions for how research strategies may be adapted to address some of the problems are discussed. LAY SUMMARY: Online surveys offer a convenient way to recruit large numbers of participants for autism research. However, the resulting samples may not fully reflect the autism population. Here we investigated the samples of 36 autism studies that recruited participants online and found that the demographic composition tended to deviate from what has been reported about the autism population in previous research. The results may thus not be generalizable to autism in general.
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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 | 0.199 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".