Bibliographic record
Abstract
The academic, financial, and social pressures that emerge when adults begin university may lead them to encounter mental health challenges (American College Health Association [ACHA], 2016). In Ontario, 46.2% of university/college students have reported ‘excessive’ stress whereas 64.5% report ‘overwhelming’ anxiety (ACHA, 2016). One approach that may be useful for enhancing mental health is exergaming which uses motion-sensor technology requiring players to move their bodies to complete the videogame sessions (Huang et al., 2017). However, studies focused on the effectiveness of exergaming for improving mental health of university students are scarce in number and limited in scope. The overall purpose of this dissertation was to examine the role of exergaming in relation to mental health reported by university students. In Study 1, a synthesis of published intervention research using exergaming to change well-being – a psychological concept affiliated with mental health – reported by adults was conducted. Results from Study 1 indicated that less than 50.0% of the coded studies - using either multi- or single-group (pre-post-test) research designs - reported enhanced well-being as a function of exergaming. In Study 2, the link between the amount and intensity of exergaming behaviour per week and mental health reported by university students was evaluated. Results from Study 2 indicated that university students who reported more frequent exergaming behaviour during a typical week displayed lower anxiety and stress plus higher flourishing and vitality. In Study 3, the contributions of exergaming to mental health of university students were investigated using an experimental research design that compared exergaming against other physical activity behaviours and self-management approaches. Results from Study 3 showed that ‘exergamers’ did not present higher or lower mental health scores compared against ‘traditional’ exercisers but displayed less stress and anxiety when compared against pet therapy, as well as, higher vitality plus lower stress and anxiety compared to ‘non-exercisers’. The results from these studies make it apparent that exergaming may not offer anything ‘unique’ as a stimulus beyond more traditional modes of exercise, but it fosters mental health of university students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".