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Record W4327616643 · doi:10.3389/fsoc.2023.1061872

Older adults' perceptions of the risks associated with contemporary gambling environments: Implications for public health policy and practice

2023· article· en· W4327616643 on OpenAlexaff
Hannah Pitt, Simone McCarthy, Samantha Thomas, Melanie Randle, Sarah Marko, Sean Cowlishaw, Sylvia Kairouz, Mike Daube

Bibliographic record

VenueFrontiers in Sociology · 2023
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsConcordia University
FundersAustralian Research CouncilDeakin UniversityAustralian Government
KeywordsReflexivityThematic analysisPublic healthPerceptionRisk perceptionPoliticsPublic relationsPsychologyPublic policyQualitative researchMarketingSociologySocial psychologyBusinessMedicinePolitical scienceEconomic growthSocial scienceEconomicsNursing

Abstract

fetched live from OpenAlex

Introduction: Rapid changes in the Australian gambling environment have amplified the risks for gamblers and pose significant threats to public health. Technological advances, saturation of marketing, and the embedding of gambling in sport have all contributed to significant changes in the gambling risk environment. Older adults have witnessed the changes to the way gambling is provided and promoted in public spaces, but little is known about how these changes have shaped the way they conceptualize the risks associated with gambling. Method: Guided by critical qualitative inquiry, semi structured interviews were conducted with 40 Australian adults aged 55 years and older, who had gambled at least once in the last 12 months. Reflexive thematic analysis was used to interpret the data. Results: Participants discussed gambling environments in Australia and how they had changed through the proliferation of gambling products, environments, and opportunities; the risks posed through the embedding of gambling in community and media environments; the role of technology in gambling environments; and the role of marketing and promotions in the changing gambling environments. Participants recognized that these factors had contributed to gambling environments becoming increasingly risky over time. However, despite the perception of increased risk, many participants had engaged with new gambling technologies, products, and environments. Discussion: This research supports the adoption of public health responses that include consideration of the environmental, commercial, and political factors that may contribute to risky gambling environments.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.372

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 studies0.0000.001
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 head0.212
GPT teacher head0.455
Teacher spread0.243 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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