Exploring shame and guilt in emotion-focused coping among emerging adult gamblers through structural equation modelling
Bibliographic record
Abstract
This study assesses the interrelationships between problem gambling, feelings of shame and guilt, and coping styles among an emerging adult sample from the University of Manitoba. Exploratory Factor Analysis (EFA) was first used to test the factor structure of the Coping with Gambling Loss measure proposed by Yi and Kanetkar (2011), and then Observed Path Models were used to test for model fit, strength of relationships, and presence of a mediated relationship of problem gambling on coping styles through either shame or guilt. Results from the EFA show a five-factor model, differing from the six-factor model proposed by Yi and Kanetkar, and despite poor initial results model fit was achieved through the observed path models. Results of the study show that problem gambling is more strongly associated with feelings of shame than feelings of guilt, feelings of guilt are more strongly associated with both avoidant coping strategies and non-avoidant coping strategies than feelings of shame, and feelings of shame and guilt mediate the relationships between problem gambling and avoidant and non-avoidant coping. The implications for the study support further testing of the model on differing demographic samples, as differing results were found compared to prior work on a normative Canadian sample, pointing to differing emotional experiences and coping methods based on demographic characteristics and group experiences.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".