Perceptions of Gambling in Tamil and Chinese Communities in Australia: The Role of Saving Face in Perpetuating Gambling Stigma and Hindering Help Seeking
Bibliographic record
Abstract
Despite Australia being a multicultural country, there is limited evidence regarding ethnic perspectives about gambling, problem gambling, and associated help seeking. The aim of this paper is to examine the role and nature of stigma in relation to gambling, in particular its association with the concept of saving face. Interview data were thematically analysed from a study investigating the attitudes and experiences of gambling among ethnic minority communities (Tamil and Chinese) in Melbourne, Victoria. By using two well-established frameworks for understanding stigma, we examine the extent to which gambling is stigmatized within these two communities. The desire to save face (a form of stigma management) significantly influences what is said about gambling and the reluctance of individuals to seek help should it become problematic. Thresholds for stigmatized behaviour appear to differ between the communities studied, as well as from what is known about the Anglo-Celtic majority. Understanding this heterogeneity may be important for informing more effective, tailored interventions.Bien que l’Australie soit un pays multiculturel, il existe peu de données probantes sur les perspectives ethniques du jeu, le jeu problématique et la demande d’aide connexe. L’objectif du présent article est d’examiner le rôle et la nature du stigmate associé au jeu, en particulier son lien avec le concept de sauver la face. Les données d’entrevues réalisées dans le cadre d’une étude sur les attitudes et les expériences de jeu de membres de communautés ethniques minoritaires (tamiles et chinoises) à Melbourne, en Australie, ont été analysées par thème. À l’aide de deux cadres de travail reconnus pour la compréhension du stigmate, nous avons examiné la mesure dans laquelle le jeu est stigmatisé dans ces deux communautés. Le désir de sauver la face (qui est une forme de gestion du stigmate) a une grande incidence sur ce qui est dit sur le jeu et sur la réticence à demander de l’aide lorsque le jeu devient problématique. Le seuil de stigmatisation du comportement semble être différent dans les deux communautés étudiées, ainsi que de ce que l’on sait du stigmate dans la communauté majoritaire anglo-celte. Comprendre cette hétérogénéité pourrait aider à élaborer des interventions personnalisées plus efficaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".