Supporting children’s healthy socio-emotional development through play: Book of Beasties – the mental wellness card game
Bibliographic record
Abstract
Play in its variety of forms is intrinsically fun , rousing positive emotions which affect children's wellbeing and mental health. In fact, play is considered such an essential component of children's healthy development that it is recognised by the United Nations Convention on the Rights of the Child (UNCRC) as a right of every child (UN, 1989). Play has been broadly defined as any activity that displays features of non-literality, positive affect, flexibility and intrinsic motivation (Krasnor and Pepler, 1980). Simply put, it is enjoyable, voluntary and done for its own sake. The enormous physical, cognitive, social and psychological benefits of play for children from infancy to adolescence are well documented. A body of evidence indicates that incorporating a playful learning approach in the classroom is a highly effective pedagogical strategy for improving academic outcomes and increasing motivation (Weisberg et al, 2013). Moreover, pedagogies based on guided play (essentially child directed but incorporating adult-scaffolded learning objectives) have been shown to have a positive impact on socio-emotional development and emotional regulation (Ogan and Berk, 2009). Book of Beasties (BoB) – an award-winning school-based intervention which aims to develop children's emotional literacy and support wellbeing – utilises a guided-learning approach through the medium of a traditional card game. Before this chapter explores BoB further, the concept of play and emerging modes of play for new digital natives (NDNs) will be briefly considered. A radical shift in the nature of play has been linked with the exponential growth of digital technologies. Children growing up in digitally wealthy societies are exposed to technologies from an increasingly young age. A Canadian study which tracked nearly 2,500 two-year-olds revealed that their average weekly screen time was 17 hours, with some toddlers amassing up to 28 hours in a seven-day period (Madigan et al, 2019). Research suggests that electronic and virtual toys have diminished the popularity of physical, traditional modes of play, including board and card games typically played with others. A European Union study (Chaudron et al, 2018) revealed that, by the age of eight, girls in particular were ready to abandon play involving physical toys for digital alternatives. Changes in conventional play habits have been associated with a decline in the child-directed free play afforded by traditional toys and games (Healey and Mendelsohn, 2019).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.250 | 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".