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Record W7043054342

The relationship between online gaming and wellbeing among post-secondary students

2022· dissertation· en· W7043054342 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityFeelingVariety (cybernetics)Association (psychology)EntertainmentWell-beingSocial relationship
DOInot available

Abstract

fetched live from OpenAlex

As the “fastest growing form of entertainment in the world” (Tran, 2019, p. 76), gaming has become a significant part of our society (Groening & Binnewies, 2019). Considering its widespread popularity as a leisure activity amongst adolescents and adults (Entertainment Software Association of Canada, 2020), it is unsurprising that multiple studies have explored its relationship to the player’s wellbeing. Previous research has found mixed findings regarding gaming’s impact on wellbeing. Several findings have identified gaming as a way to relieve stress, relax (Russoniello et al., 2009; Snodgrass et al., 2011; Wack & Tantleff-Dunn, 2009), positively influence aspects of social wellbeing (Gitter et al., 2013; Kowert & Oldmeadow, 2015; Martončik & Lokša, 2016) and is associated with a variety of improvements in psychological and physiological functions (Ryan et al., 2006). Despite these benefits, numerous other findings have associated gaming with negative outcomes such as interfering with a player’s social functioning, wellbeing, and adjustment (Grüsser et al., 2007; Stockdale & Coyne, 2018; Weinstein, 2010). Given these apparent contradictions in previous literature, further exploration needs to be conducted in understanding the relationship of gaming and wellbeing among post-secondary students. To examine this relationship, additional factors that can impact one’s wellbeing should be considered such as the motives for engaging in their leisure pursuits, one’s feelings of connection and support from others in the community, and the breadth of activities one engages in. The purpose of this study was to explore the relationship between gaming and wellbeing among post-secondary students while taking into account the player’s motivation, social connectedness, and overall leisure repertoire. A secondary data analysis was undertaken using data (n = 982) gathered from the Georgian College Student Wellbeing Survey launched in January 2019 conducted by the Canadian Index of Wellbeing (CIW). Multiple factors were considered in exploring the relationship to wellbeing including the students demographic characteristics (age, sex, student status). Students identified the frequency and intensity of their gaming, a measure of their leisure repertoire was calculated, and the degree to which they were socially motivated to participate in their leisure assessed. Three different measures were used to assess social connectedness: (1) number of close friends, (2) feelings of social isolation, and (3) sense of community (i.e., social climate and bonds). Finally, as a measure of their subjective wellbeing, students rated their life satisfaction along an 11-point scale. The findings indicated that neither whether students participated in gaming nor their intensity of gaming were significant factors in explaining wellbeing. Instead, social factors (feelings of social isolation and perceptions of social climate and bonds) and leisure repertoire were particularly significant factors in explaining their wellbeing. Reducing feelings of social isolation emerged as the most important factor in explaining wellbeing irrespective of how intensely or how often students participate in gaming. Ultimately, social context is the most important factor in explaining variations in wellbeing, above and beyond other factors including gaming participation and intensity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.016
GPT teacher head0.279
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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