Evaluating More Granular Options for Socio‐Demographic Questions in Autism Research
Bibliographic record
Abstract
We evaluated the feasibility and acceptability of adding more detailed choices for race, ethnicity, sex, gender, and socio-economic status for a demographic survey used by families both within and outside a large learning health network, the Autism Care Network (ACNet). We updated our demographic survey using an iterative approach, incorporating qualitative and quantitative feedback from interested parties across the US and Canada. Pilot testing of the revised survey was conducted with families with and without autism served by two large academic pediatric tertiary care centers. Through purposive sampling, recruitment was enriched for families from ethnic, racial, or gender minority backgrounds. The updated demographic survey increased the number of response options for race and ethnicity, sex, gender, and language. 85 families within the ACNet and 242 families outside the ACNet provided feasibility and acceptability data. 41% of respondents were from nonWhite or multiple race groups. 99% of respondents rated the updated form same or better than the original. 91% of respondents rated the updated form as acceptable, while 97% rated the survey as feasible. Despite concerns about the burden on respondents, we found high rates of feasibility and acceptability of more granular response options in demographic surveys. Researchers can adapt this approach to make their own more granular demographic forms focused on the specific variables relevant to their study and local contexts. More granular demographic data can identify strengths and gaps in representation that could impact a study's generalizability.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".