“It’s never black and white”: Early interventionists’ experiences supporting abused children and their families.
Bibliographic record
Abstract
Early interventionists (EIs) support families of infants and toddlers with delays and disabilities. Children with disabilities are a high risk for abuse and neglect. To understand how or whether trauma-informed practices are utilized in early intervention, 28 EIs participated in focus groups to share their experiences, challenges, and strategies when supporting children with disabilities who have also been abused. A thematic analysis was used to analyze the data. Participants indicated that they experienced challenges related to identifying signs and symptoms of child abuse and neglect, making decisions regarding their role as a mandated reporter and differentiating between family circumstances (i.e., poverty) and abuse and neglect. Participants also indicated that their educational programs did not prepare them to utilize trauma-informed practices or prepare them to work with children who have been abused or neglected. Overall, the EI system supports individuals with disabilities who are more vulnerable to abuse, neglect, and maltreatment. However, this study indicates EIs do not feel prepared or confident to support children who have experienced abuse. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".