Perceptions and Acceptability of a Smartphone App Intervention (ChildSafe) in Malaysia: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Home is a vulnerable place for accidental child injuries. Unintentional injuries are a leading cause of death, hospitalization, and disabilities. These injuries are considered preventable and if not tackled, they will continue to be a persisting problem. Smartphones have become increasingly important in our everyday life and is an important tool not only for communication but also for other purposes-they have apps that can be used for various purposes. Therefore, an app-based intervention (ChildSafe) was developed to assess and reduce child injury at home. OBJECTIVE: This study aimed to evaluate the acceptance of the ChildSafe smartphone app intervention by parents/guardians. METHODS: This study was conducted using a qualitative exploratory approach on selected participants of the ChildSafe intervention app study. A total of 27 semistructured in-depth interviews were carried out among parents or guardians who have at least one child between the age of 0 and 59 months in the area of Sungai Buloh, Selangor, between November 2017 and March 2018. Interview questions were developed from the consolidated framework for implementation research (CFIR). Interviews were recorded, transcribed verbatim, and data were thematically analyzed guided by CFIR. RESULTS: The study revealed users' perception on usability, feasibility, and acceptability toward the ChildSafe app. Three CFIR domains were identified: intervention characteristics, inner setting, and characteristics of individuals. A total of 5 constructs were revealed under intervention characteristics: evidence strength and quality, relative advantage, adaptability, trialability, and design quality and packaging; 2 under inner setting: implementation climate and readiness for implementation; and 4 under characteristics of individuals: knowledge and beliefs about the intervention, self-efficacy, individual stage of change, and other personal attributes. In general, participants felt the app is extremely useful and effective, easy to use, and purposeful in achieving home safety assessment via reminders. The app replaces the need for participants to search for information on home safety and dangers, as the app itself was designed as a tool to assess for this specific purpose. Even at the nascent stage and despite its limitations, the app has prompted users to consider and make changes around their own home. However, future versions of the app should be expanded to make it more attractive to users as it lacks interactive feedback and additional features. CONCLUSIONS: Parents/guardians are accepting the use of the ChildSafe app to prevent child injury at home. However, further expansion and improvements are needed to increase the acceptability of this app by parents/guardians.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".