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Record W2141220480 · doi:10.1111/1467-9566.12194

Living for the moment: men situating risk‐taking after the death of a friend

2015· article· en· W2141220480 on OpenAlexafffund
Genevieve Creighton, John L. Oliffe, Eva McMillan, Elizabeth Saewyc

Bibliographic record

VenueSociology of Health & Illness · 2015
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsTragedy (event)Psychological interventionPsychologySuicide preventionInjury preventionSocial psychologyEmbodied cognitionGerontologyPoison controlMedicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

The primary cause of death for men under the age of 30 is unintentional injury and, despite health-promotion efforts and programme interventions, male injury and death rates have not decreased in recent years. Drawing on 22 interviews from a study of men, risk and grief, we describe how a risk-related tragedy shaped the participants' understandings of and practices of risk-taking. The findings indicate that most participants did not alter their perceptions and engagement in risky practices, which reflected their alignment to masculine ideals within specific communities of practice where risk-taking was normalised and valorised. Continued reliance on risky practices following the death of a friend was predominantly expressed as 'living for the moment,' where caution and safety were framed as conservative practices that undermined and diluted the robustness ideally embodied by this subgroup of young men. Two main themes: living life, accepting death and upping the ante illustrate how risk-taking can persist following a death. A smaller group of participants articulated a different viewpoint; reining in risk practices, to describe their risk management approaches after the death of a male friend. This novel study confirms the ongoing challenge of reducing men's risk-taking practices, even after the death of a friend.

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)0.005
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 score0.235
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.372
Teacher spread0.318 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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