Use of technology in evidence-based programs for child maltreatment and its impact on parent and child outcomes
Bibliographic record
Abstract
Introduction: Technology has been used in evidence-based child maltreatment (CM) programs for over a decade. Although advancements have been made, the extent of the application of technology in these programs, and its influence on parental and child outcomes, remains unclear within the context of changes that emerged because of the COVID-19 pandemic. This scoping review provides a contextualized overview and summary of the use of technology in evidence-based parenting and child programs serving families impacted by child maltreatment and the effects of technology-enhanced programs on target outcomes. Materials and methods: Using Arksey and O'Malley's methodological framework, we searched seven databases to identify peer-reviewed and grey literature published in English from 2000 to 2023 on evidence-based programs, according to the California Evidence-Based Clearinghouse (CEBC), that included technological supports for two populations: at-risk parents for child maltreatment prevention, and children and youth 0-18 years exposed to child maltreatment. All study designs were included. Results: = 8 on child-level programs). Four studies were published in the context of COVID-19. Two main uses of technology emerged: (1) remote programmatic delivery (i.e., delivering all or part of the program virtually using technology) and (2) programmatic enhancement (i.e., augmenting program content with technology). Improvements across parenting and child mental health and behavioral outcomes were generally observed. Discussion: Technology use in evidence-based child maltreatment programs is not new; however, the small sample since the start of the COVID-19 pandemic in this review that met inclusion criteria highlight the dearth of research published on the topic. Findings also suggest the need for the inclusion of implementation outcomes related to adoption and engagement, which could inform equitable dissemination and implementation of these programs. Additional considerations for research and practice are discussed.
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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 | 0.035 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".