Group Treatment for Women Gamblers Using Web, Teleconference and Workbook: Effectiveness Pilot
Bibliographic record
Abstract
While the past decades have seen a dramatic increase in the number of women who gamble and develop consequent problems, treatment services are being underutilized in Ontario. This pilot study explores the feasibility of using web- and phone-based group interventions to expand services available for women who might not otherwise seek or be able to access treatment. Distinct treatment considerations for working with women, such as the value of a women's group, advantages of phone counselling, and the implementation of modern web-based services, were reviewed. The study involved a clinician-facilitated group that used teleconferencing and webinar technology (Adobe Connect) for support and discussion, and a Tutorial Workbook (TW) developed specifically to address the issues and treatment needs of women who gamble at a problematic level. A mixed method analysis used to evaluate the results suggested that the group-based teleconference/webinar approach provided a much-needed means of treatment support for women. Participants reported that the program helped them to understand their gambling triggers, to improve their awareness, to feel better about themselves, to modify their mood and anxiety levels, to feel less isolated, to address their relationships, and to feel more hopeful for the future. The Tutorial Workbook, which was used to supplement the educational component of the group interaction, was highly rated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".