Practitioners’ and Policymakers’ Successes, Challenges, Innovations, and Learning in Promoting Children’s Well-being During COVID-19: Protocol for a Multinational Smartphone App Survey
Bibliographic record
Abstract
BACKGROUND: The advent of COVID-19 abruptly thrust the health and safety of children and families into greater risk around the world. As regional and local governments, nongovernmental organizations, communities, families, and children grapple with the immediate public health impact of COVID-19, the rights and well-being of children, especially those who are already marginalized, have been overlooked. Those working with children have likely encountered unprecedented challenges and responded in innovative ways in efforts to address the needs and rights of all children. OBJECTIVE: This paper presents a protocol for a large-scale, multinational study using a new smartphone app to capture the real-time experiences and perspectives of practitioners and policymakers supporting children and families during the COVID-19 pandemic around the globe in relation to a children's human rights 4P framework of protection, provision, prevention, and participation. METHODS: This protocol describes a mixed methods survey utilizing a custom-built iOS and Android smartphone app called the COVID 4P Log for Children's Wellbeing, which was developed in close consultation with 17 international key partner organizations. Practitioners and policymakers working with and for children's well-being across 29 countries and 5 continents were invited to download the app and respond to questions over the course of 8 weeks. The anticipated large amount of qualitative and quantitative response data will be analyzed using content analysis, descriptive statistics, and word frequencies. RESULTS: Formal data collection took place from October 2020 until March 2021. Data analysis was completed in July 2021. CONCLUSIONS: The findings will directly inform the understanding of the ways in which COVID-19 has impacted practitioners', managers', and policymakers' efforts to support children's well-being in their practices, services, and policies, respectively. Innovative and ambitious in its scope and use of smartphone technology, this project also aims to inform and inspire future multinational research using app-based methodologies-the demand for which is likely to continue to dramatically rise in the COVID-19 era. Mitigating the risks of longitudinal remote data collection will help maximize the acceptability of the app, respondents' sustained engagement, and data quality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31013.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".