Meeting the Unique Treatment Needs of Female Gamblers: A Workbook and Webinar Effectiveness Pilot Study
Bibliographic record
Abstract
While 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, especially by women.\nThis pilot aimed to be responsive to this concern, circumventing barriers by inviting access to support through the use of two interventions. One was a mailed self-help workbook designed for women. The second utilized the workbook in a clinician facilitated group context using teleconferencing in conjunction with webinar technology. This small pragmatic pilot aimed to both get feedback on the helpfulness of the workbook and to explore the feasibility of using web and phone based group interventions.\nThe presentation will takes us on a brief tour of current research knowledge about barriers to treatment, and highlight the distinct treatment considerations in working with women, the value of a women’s group, the increasingly utilized role and advantages of phone counselling, the modern explosion of web based services and the added value of self-help materials for gamblers. We’ll review the content and rationale of the self-help workbook, a manual drawing from best practices and research in diverse areas relevant to women’s recovery.\nA mixed method analysis found that the interventions were well received by women in both Canada and the USA. We’ll review both qualitative and quantitative results. This innovative and original treatment intervention holds considerable promise in expanding options for women who might not otherwise seek or be able to access treatment.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".