Cognitive-Behavioral Therapy for a Refugee Mother With Depression and Anxiety
Bibliographic record
Abstract
This case study illustrates a short-term cognitive behavioral therapy (CBT) for a refugee single mother of a 4-year-old son to address depression and anxiety symptoms. Although she has histories of multiple trauma experiences such as sexual abuse and intimate partner violence, the client preferred to focus on current difficulties rather than trauma histories. As such, non-trauma-focused CBT utilizing psychoeducation, skill building, activity monitoring and scheduling, and cognitive restructuring is implemented over 10 individual sessions. The client’s progress was measured by the Depression Anxiety Stress Scale (DASS-21), the Quality of Life Enjoyment and Satisfaction Questionnaire–Short Form (Q-LES-Q-SF), and a full-length Columbia-Suicide Severity Rating Scale (C-SSRS) at the intake, midpoint, and last session. The client showed improvement in all measures after the treatment, which corresponded with the client’s verbal reports during the session. This case illustrates the critical clinical decision-making points made by the therapist, and recommends the evidence-based practice protocol that considers empirically supported treatments for the comorbidity of depression and anxiety with multiple trauma experiences, the client preference, and contextual factors in addressing complex clinical presentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".