Accuracy and acceptability of eHealth data collection for an early intensive behavioral intervention program
Bibliographic record
Abstract
Abstract Early intensive behavioral intervention (EIBI) is a treatment designed to increase adaptive behavior and decrease maladaptive behaviors for children with autism spectrum disorder (ASD). EIBI service providers typically collect data using pen‐and‐paper. Participants were four service providers employed at a large community‐based EIBI program. Differences in accuracy between collecting discrete‐trial‐teaching (DTT) data and challenging behavior data using pen‐and‐paper and an eHealth electronic data collection (EDC) application were assessed. The social validity of both methods of data collection was also examined. Pen‐and‐paper and EDC were equally accurate, but participants preferred using pen‐and‐paper. Our accuracy findings agreed with previous comparisons of EDC and pen‐and‐paper. Both methods of data collection are viable for an EIBI program; however, social validity considerations will determine the ease of EIBI programs transitioning to using an eHealth tool for data collection.
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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 | 0.087 | 0.336 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".