gamified sleep intervention for veterans: an overview
Bibliographic record
Abstract
A good night’s sleep is well known to be imperative for maintaining one’s overall wellness. Yet, about half of Canadian adults struggle with falling asleep or maintaining sleep. The impacts of insufficient sleep are wide-ranging, from physiological correlates such as diabetes to mental correlates such as depression. Effective treatments for sleep-related issues exist: for example, online interventions for insomnia have been found to be effective. As a medicine and a health psychology student at, respectively, Sherbrooke University and McGill University, we worked on the MissionVAV health promotion program during the COVID-19 pandemic, providing free gamified interventions for Canadian Veterans and their families. Over the course of several online interventions related to physical health, we observed that a large proportion of our participants were dissatisfied with their sleep. Consequently, we have developed an 8-week online sleep intervention to address this primordial element of primary prevention. The intervention aims to better our participants’ sleep through providing weekly readings on the following topics: age-related changes in sleep, proper sleep hygiene, varied relaxation techniques as well as the relationship between sleep and chronic pain, menopause, shift work, rumination, exercise and light. To promote healthy sleep hygiene habits, daily self-assessment questions are provided and are incentivized through points and storytelling. Furthermore, health coaches trained in sleep medicine follow participants throughout their journey to provide support and reinforcement. Ultimately, the intervention aims to shed light on the importance of sleep within preventative medicine, tackling it systematically in an engaging, gamified fashion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".