Significant life events and social connectedness in Australian women’s gambling experiences
Bibliographic record
Abstract
Aim: The aim is to examine significant life events and social connections that encourage some women to gamble. Specifically, how do these events and connections described as important for women who develop gambling-related problems differ for women who remain recreational gamblers? Design: 20 women who were electronic gaming machine (EGMs, poker machines, slots) players were interviewed using a brief interview guide. They also completed the nine question Problem Gambling Severity Index (PGSI) from the Canadian Problem Gambling Index CPGI). 11 women self-identified as recreational gamblers (RG) while 9 had sought and received help for their gambling problems (PG). Using a feminist, qualitative design and an adaptive grounded theory method to analyze their histories, a number of themes emerged indicating a progression to problem gambling for some and the ability to recognise when control over gambling was needed by others. Results: Although both groups (RG and PG) reported common gambling motivations differences appeared in the strength of their social support networks and ways of coping with stress, especially stress associated with a significant life event. Conclusions: The human need for social connectedness and personal bonds with others emphasised the usefulness of using social capital theories in gambling research with women.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".