Exploring Parental Intentions to Use Digital Tools to Enhance Parent-Child Sexual Communication in Europe: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Parent-child communication about sexuality education is critical for safe adolescent sexual development and well-being. Yet, there is evidence that these conversations are often ineffective. Digital tools have therefore emerged as promising interventions that may assist parents in addressing sensitive or difficult topics. However, our understanding of the factors that may motivate parental adoption of these technologies remains limited. OBJECTIVE: This study aimed to explore factors associated with European parents' intentions to use a digital tool designed to support parent-child sexual communication and complement school-based sexuality education. The study was conducted across the United Kingdom, Belgium, and Italy. METHODS: Using the technology acceptance model, we applied structural equation modeling to identify motivators of parents' intention to use the hypothetical app. This included perceived usefulness and perceived ease of use. Perceived usefulness was further analyzed by its subcomponents-relevance to parenting and quality of technology-through an alternative 3-construct model. Additionally, the associations between demographic characteristics (age, gender, country of residence, and education level) and the latent constructs were assessed. RESULTS: =0.47. Perceived usefulness was significantly associated with intention to use (β=0.67, P<.001), while perceived ease of use showed no significant association with either intention to use (β=-0.01, P>.05) or perceived usefulness (β=0.10, P>.05). An alternative 3-construct analysis revealed that relevance to parenting and quality of technology were both independently significantly associated with intention to use (β=0.09, P<.001 and β=0.59, P<.001, respectively). Demographic characteristics were also significantly related to the technology acceptance model constructs in the model. CONCLUSIONS: These findings highlight the critical role of perceived usefulness, specifically relevance to parenting needs and the perceived quality of technology, in shaping parental intentions to use digital parent-child sexual communication tools. Developers of educational digital technologies should therefore prioritize high-quality design features to inspire usage. Future research should evaluate real-world digital tools to assess actual usage, long-term engagement, and their effectiveness in enhancing parent-child sexual communication.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".