Children’s access to child protection social work through mobile apps
Bibliographic record
Abstract
Abstract Direct reporting of child abuse by children themselves is rare. Children’s communication is increasingly managed through mobile phones and associated apps. Yet little is currently known about how statutory child protection services might be accessed through apps to support direct reports, or to find out about statutory child abuse responses. The objective of the present study was to identify the characteristics of apps that included reference to child abuse and to understand how a child might report if they had concerns. Searches of Google and Apple app stores were conducted at four intervals over a nine-year period from 2014 to 2023. Developer descriptions of apps containing terms relevant to child abuse were thematically analysed according to the app categorization in the store, target user, and child abuse report function. A total of 258 apps met the child abuse app inclusion criteria, <001 percent of all apps available. Just over a third were targeted at children, and a quarter at professionals. Seventeen applications enabled the reporting of child abuse concerns, of which six were aimed at children. Barriers to access included organizational membership and locality. Apps continue to rely on the NGO sector, schools, and health (i.e. adults) to report abuse.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".