A Qualitative Study of the Supports Women Find Most Beneficial When Dealing With A Spouse's Sexually Addictive or Compulsive Behaviors
Bibliographic record
Abstract
While not included in the diagnostic classifications of sexual disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM), sexual addictions and compulsivities are increasingly being encountered by mental health professionals, and the field of sexual addictions is gaining wider acceptance (Delmonico & Carnes, 1999; Garos, 1997). Research shows the majority of people struggling with sexual addictions and compulsivities involving the Internet are married, heterosexual males (Cooper, Delmonico, & Burg, 2000), and that women who are married to these men are directly impacted by this problem (Schneider, 2000b). Although there is research on many issues relating to wives of sex addicts, there is currently a void in the research literature regarding the kinds of support women find most beneficial when dealing with a spouse's sexually addictive or compulsive behaviors. The primary purpose of this qualitative study was to identify the kinds of support women find most beneficial when dealing with a spouse's sexually addictive and compulsive behaviors. Using grounded theory methodology (Strauss & Corbin, 1990), twenty-two women from Canada and the United States were interviewed to find out what they found most helpful as they sought support to cope with a spouse's sexually addictive and compulsive behaviors. A theory of support for this population emerged from the data. It was found that women in affected marriages utilize two main categories of support, namely, coping supports, and change-oriented supports such as Recreational, Relational, Professional, Spiritual and Conceptual. Responding to problematic sexual behavior is moderated by numerous internal and external factors. Five common factors of positive support experiences were identified across the sample. These five common factors were: (a) Connection, (b) Advocacy, (c) Validation, (d) Education, and (e) Direction (CAVED). This study did not succeed in identifying distinct pathways of support for women who discover this problem versus those who have it disclosed to them, nor did this study identify specific treatment modalities that are effective. The results of this study provide a useful template for clinicians, support networks and researchers to begin understanding what is helpful and unhelpful when working with this population.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".