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Record W4307884509 · doi:10.1145/3549514

Shoulder-to-Shoulder: How Pinball Supports Men's Wellbeing

2022· article· en· W4307884509 on OpenAlexaff
Daniel Johnson, Katelyn Wiley, Cale J. Passmore, Ella Horton, Roger Altizer, Regan L. Mandryk

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMental healthSocial connectednessPsychological interventionThematic analysisSituatedContext (archaeology)PsychologySocial psychologyAppealSociologyQualitative researchPublic relationsPsychotherapistPolitical scienceComputer science

Abstract

fetched live from OpenAlex

When facing mental health concerns, men seek help less, confront greater stigma in accessing treatment, and experience more severe consequences. Interventions targeted at men are often grounded in activity and situated in appealing contexts, such as sporting or gaming spaces. In this paper, we question how pinball---a social tangible and digital leisure activity---can support men's mental health and wellbeing, through thematic analysis of in-depth semi-structured interviews (n=15) with male pinball enthusiasts. Our contribution is threefold: first, we evidence pinball as a context that provides incidental benefits to mental health directly, and indirectly through social connectedness; second, that enthusiasts actively enhance their social lives and resulting wellbeing via pinball; and third, that pinball contexts are suited for designing interventions that provide mental health supports within subcultures that appeal to men. We situate our findings in theories of wellbeing, activity-based communication, shoulder-to shoulder self-disclosure, and the importance of third-spaces for social wellness.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.334
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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