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Record W3201381096 · doi:10.1080/2159676x.2021.1979635

Making sense of humour among men in a weight-loss program: A dialogical narrative approach

2021· article· en· W3201381096 on OpenAlexaff
Timothy Budden, James A. Dimmock, Brett Smith, Michael Rosenberg, Mark R. Beauchamp, Ben Jackson

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2021
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDialogical selfNarrativeSense (electronics)PsychologyWeight lossArtLiteratureSocial psychologyMedicineObesityEngineeringInternal medicine

Abstract

fetched live from OpenAlex

Humour appears to be an important aspect of health-promoting efforts for some men. A better understanding of the role humour plays in men’s health contexts may provide insight into the optimal design of health interventions for men. In this study, we explored the role banter, humour that blurs the line between playfulness and aggression, plays for men in a men’s weight loss context. We applied dialogical narrative analysis to thirty interviews conducted with men involved in a men’s weight-loss program that leverages competition to drive weight loss. Banter served several functions for men in the program, including allowing them to determine their social position during early group formation, feel good, develop camaraderie, experience respite, provide male inter-personal support in a counter-intuitive way, and ‘be themselves’. Men could use banter as a tool to develop resilience for themselves, but could also adapt their approach to use banter as a means of providing support for others. Banter could also cause trouble, through conflict and misunderstandings, primarily understood through a lens of narratives of progressiveness, inclusiveness, and a ‘changing culture’. Banter could do harm, by positioning oneself against certain characteristics, and as a tool to get under people’s skin. However, an approach-orientation to one’s problems may allow misunderstandings that arise due to banter to lead to enhanced group cohesion. Intervention developers ought to explicitly address the potential for banter (and humour more broadly) to have positive and negative effects in men’s health contexts.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.395
GPT teacher head0.605
Teacher spread0.210 · 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 designQualitative
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

Citations11
Published2021
Admission routes1
Has abstractyes

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