Computerized behavioral activation treatment for major depressive disorder and the effects on sexual desire
Bibliographic record
Abstract
The present study was designed to examine the effects of a computerized behavioral activation treatment program on sexual desire, sexual behavior, and depression symptoms. Seven adults who met criteria for either major depressive disorder or dysthymic disorder were recruited from Kalamazoo, Portage, and surrounding areas in Southwestern Michigan. All participants completed at least five sessions of behavioral activation treatment, and six out of seven participants completed all ten sessions. Symptoms of depression, sexual desire, and sexual behavior were assessed at pretreatment and before each treatment session through a combination of the Beck Depression Inventory – II (BDI-II), the Revised Hamilton Rating Scale for Depression (RHRSD), the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-I), and the Sexual Desire Inventory (SDI). It was hypothesized that participants would report an improvement in overall depression. It was further hypothesized that participants would report an increase in sexual desire and sexual behavior frequency after completing the depression treatment program. Results were explored statistically using Pearson Product Moment Correlations of variables, paired two sample t-tests of pretest and posttest treatment data, and visual inspection of individual participant scores over the course of treatment. Results indicated a significant improvement in depression that is both statistically and clinically significant. Additionally, no significant improvement to sexual desire, nor an increase in sexual behavior frequency, was noted as a result of completing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".