Child Maltreatment Disclosure to a Text Messaging–Based Crisis Service: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Disclosure is a difficult but important process for victims of child maltreatment. There is limited research on child maltreatment disclosure. Young people have been reluctant to disclose victimization to adults, but short message service (SMS) crisis services may represent one novel method of engaging young people around sensitive topics. OBJECTIVE: The purpose of this study was to determine characteristics of child maltreatment disclosure to an SMS-based crisis service. METHODS: We conducted a content analysis of all conversations (N=244) that resulted in a mandatory report by an SMS-based crisis service between October 2015 and July 2017. We coded characteristics of the disclosure process, including the reason for initial contact, phrase used to disclose abuse, perpetrator, type of abuse, and length of victimization. After identifying terms used by young people to disclose child abuse, we randomly selected and analyzed 50 conversations using those terms to determine if use of the terms differed between conversations that did and did not result in mandatory report. RESULTS: Parents were the most common perpetrator. Physical abuse was the most common form of abuse discussed in the initial abuse disclosure (106/244, 43.4%), followed by psychological abuse (83/244, 34.0%), sexual abuse (38/244, 15.6%), and neglect (15/244, 6.1%). More than half of the texters discussed abuse or other significant family issues in the first message. An explicit description of the experience or definite language, such as abuse, rape, and molested, was common in disclosures. CONCLUSIONS: Early disclosure, combined with explicit language, may suggest at least a portion of young victims are actively seeking safe ways to talk about their experiences with abuse, rather than incidentally sharing experiences while seeking support for other issues. SMS text messaging may be a valuable way to engage with young people around sensitive topics, but these approaches will require careful consideration in their development, implementation, and evaluation to ensure a positive experience for young people.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".