Cocreating Life Pathways: Problem Gambling and its Impact on Families
Bibliographic record
Abstract
The objective of this study was to present a grounded theory specific to problem gambling and its impact on families. The research question was ‘‘How does problem gambling impact the family?’’ Twenty-two families participated in the study; most families (n = 21) were involved in treatment for problem gambling. In total, 47 interviews were conducted with 37 family members. Using the grounded theory method, the interviews were analyzed and the resulting theory is comprised of seven interrelated, dynamic, and iterative elements. The first two elements, trauma and trigger, are specific to the problem gambler. These two elements are foundational to the initiation of problem gambling and are referred as the problem gambling platform. Once problem gambling behavior is initiated, both the gambler and other family members individually and collectively experience the other five elements including transition, tension and turmoil, transformation, transcendence, and termination. Understood from a holistic perspective, the psychosocial processes described in this theory ultimately result in family members cocreating life pathways for both themselves and other family members. Representing the uniqueness of each individual family member’s journey within the context of the family environment, this theory highlights the need for relational, family-focused care, and treatment options.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".