The Grow It! app—longitudinal changes in adolescent well-being during the COVID-19 pandemic: a proof-of-concept study
Bibliographic record
Abstract
Adolescent mental health and well-being have been adversely impacted by the COVID-19 pandemic. In this preregistered longitudinal study, we evaluated whether adolescents' well-being improved after playing the multiplayer serious game app Grow It! During the first lockdown (May-June 2020), 1282 Dutch adolescents played the Grow It! app (age = 16.67, SD = 3.07, 68% girls). During the second lockdown (December-May 2020 onwards), an independent cohort of 1871 adolescents participated (age = 18.66, SD = 3.70, 81% girls). Adolescents answered online questionnaires regarding affective and cognitive well-being, depressive symptoms, anxiety, and impact of COVID-19 at baseline. Three to six weeks later, the baseline questionnaire was repeated and user experience questions were asked (N = 462 and N = 733 for the first and second cohort). In both cohorts, affective and cognitive well-being increased after playing the Grow It! app (t = - 6.806, p < 0.001; t = - 6.77, p < 0.001; t = - 6.12, p < 0.001; t = - 5.93, p < 0.001; Cohen's d range 0.20-0.32). At the individual level, 41-53% of the adolescents increased in their affective or cognitive well-being. Adolescents with higher risk profiles (i.e., more depressive symptoms, lower atmosphere at home, and more COVID-19 impact) improved more strongly in their well-being. Positive user evaluations and app engagement were unrelated to changes in affective and cognitive well-being. This proof-of-concept study tentatively suggests that Grow It! supported adolescents during the pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".