Reducing the risk of sexual abuse for people who use augmentative and alternative communication
Bibliographic record
Abstract
To date little attention has been focused on the sexual abuse experiences of people who use augmentative and alternative communication (AAC) and on addressing ways to reduce their risk for this type of abuse. This paper describes the results of a 3-year project that aimed to: (a) learn about the sexual abuse experiences of people who use AAC; (b) provide educational forums and resources on topics relating to sexual abuse for adults who use AAC; (c) define implications in risk reduction for various community service workers who support people who use AAC (e.g., attendant service providers, abuse counselors, sexual health educators, police, victim assistance services, legal professionals, and health care professionals); and (d) make recommendations to parents, educators, service providers, and consumer advocacy organizations about their roles in reducing the risk of abuse for youth and adults who use AAC. The findings suggest that the majority of participants in this project have experienced a range of abuses including sexual abuse, lack information about healthy and abusive relationships, have no way of communicating about sexuality and abuse, and lack supports in their personal lives and from within the community-at-large that are necessary to cope with relationship difficulties and specifically problems associated with abuse and justice system services. These findings and implications are shared with the intent of highlighting the need for more research and attention to the issue of abuse prevention for people who use AAC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".