Co-designing an e-resource to support’ search for mobile apps
Bibliographic record
Abstract
Background: Contemporary parents use mobile applications or “apps” to resolve their day-to-day parenting concerns. However, research suggests an abundance of low-quality apps makes the app searching process arduous for parents, therefore, there is a need to develop a resource that supports parents’ search for apps. Methods: The study aimed at engaging parents in co-developing a parenting app directory and co-designing Webpages to feature the directory. Four focus group discussions were conducted with 18 first-time Canadian parents to develop the parenting app directory. Participatory design was used to co-create Webpage prototypes (landing or main Webpage and the app description page) with 3 first-time Canadian parents. Results: Twelve apps that met the eligibility criteria were included in the parenting app directory. Parents supported the idea of creating an app directory and recommended sharing the link in perinatal classes. During design sessions, parents stressed the importance of an organized user interface, providing less but the best choices to ease the search process for apps, reducing the number of clicks to save time, and mobile optimization of the Website to accommodate different screen sizes. Conclusions: Contemporary parents’ use of apps is growing significantly; therefore, clinicians should support parents’ search for quality apps and guide them accordingly. Parents can provide insight into design principles that can be used in developing appealing parenting app resources. Parents should be involved in designing future resources as they can significantly contribute to ensuring a resource is useful.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".