Co-Designing an Infant Early Childhood Mental Health Mobile App for Early Childhood Education Teachers' Professional Development: Community-Based Participatory Research Approach
Bibliographic record
Abstract
BACKGROUND: Many young children spend at least some time in early care and education programs, where they develop social-emotional skills that prepare them for future success. However, young children may exhibit behavioral challenges in these settings, negatively impacting their social-emotional development. It is critical that the early childhood workforce is prepared to support young children's burgeoning social-emotional skills to address challenging behaviors in early care and education classrooms. Infant and early childhood mental health consultation is an evidence-informed approach for increasing teachers' skills for managing young children's emotions and behaviors. One mechanism to increase teachers' access and use of the infant and early childhood mental health consultation programs is through on-demand mobile apps. OBJECTIVE: This study aims to investigate 2 primary objectives: to document the development of the Jump Start on the Go (JS Go) app through community-based participatory research (CBPR) methodologies, and to evaluate and refine the app based on early childhood education (ECE) teacher feedback using a mixed methods assessment approach. METHODS: This study used a community-based participatory research approach to design and evaluate the effectiveness of the JS Go app across 3 phases. In phase 1, a description of how the JS Go app was developed using CBPR principles is provided. In phase 2, teachers (n=12) were interviewed after reviewing mockups of the JS Go app to gather feedback about the interface and usefulness of the app to current and new teachers. Rapid qualitative analysis generated themes to inform phase 3 (n=31) of the study. RESULTS: Phase 2 findings suggested that teachers viewed the app as aesthetically pleasing with concise information, but there were design and content features that needed to be refined to improve ease of use for accessing content. Teachers also described the app as beneficial and useful to both current and new ECE teachers and identified it as a tool to support sustainability for the use of JS practices. In phase 3, teachers rated the JS Go app favorably across all mHealth (mobile health) App Usability Questionnaire dimensions, including interface satisfaction (mean 6.12 on a 7-point scale), ease of use (mean 5.56), and usefulness (mean 5.37). Despite positive usability ratings, teachers expressed less certain intentions to adopt the app, scoring near the midpoint on the Technology Acceptance Model Instrument-Fast Form's predicted future use scale (mean 1.60, -4 to +4-point scale). Implications for how the findings were used to make adaptions to the app are discussed. The next steps for testing the efficacy of the app in a randomized control trial are described. CONCLUSIONS: ECE teachers have overall positive perceptions about the value of the JS Go app. Future research will need to test the efficacy of the app for increasing and sustaining teacher's use of JS practices.
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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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".