Online environmental scan and content analysis of social stories about needle procedures
Bibliographic record
Abstract
Needle procedures are often difficult for autistic children. Preparatory education is an evidence-based strategy that is especially important for autistic children given they commonly struggle with unpredictability. Carol Gray developed Social Stories to walk autistic children through new/challenging situations step-by-step. Although needle-related Social Stories exist online, no research has investigated whether their content aligns with best practices for needle pain and fear management and Gray's guidelines for Social Story development. OBJECTIVE: This study aimed to characterize the content of online Social Stories about vaccination and venipuncture. Specifically, the degree to which Social Stories (a) depict evidence-based/helpful and unhelpful coping strategies, (b) follow Gray's guidelines, and (c) depict accurate procedural steps, was examined in an exploratory manner. METHODS: An online environmental scan (systematic method of collecting and synthesizing information) characterized the content of Social Stories. A Google search was conducted, including free, English-language stories. After screening, two coders conducted deductive content analysis (>80 codes) with the sample of 82 eligible Social Stories; frequency statistics and quotes were derived. RESULTS: Most Social Stories focused on vaccination (89%). Social Stories commonly conveyed evidence-based strategies (e.g., 70% depicted distraction), accurate procedural information (e.g., >80% depicted step of needle insertion), and followed Gray's guidelines (e.g., 90% had meaningful titles). Several areas for improvement exist, including allowing for user customization and depicting less commonly shown evidence-based strategies like topical anesthetics. CONCLUSION: Social Stories may have utility for supporting autistic children and their caregivers through needle procedures. Examining effectiveness/outcomes of usage is a future research avenue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".