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Record W3033730235 · doi:10.1177/1534650120924128

Cognitive-Behavioral Therapy for a Refugee Mother With Depression and Anxiety

2020· article· en· W3033730235 on OpenAlexaff
Jessie Faber, Eunjung Lee

Bibliographic record

VenueClinical Case Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychoeducationPsychologyCognitive restructuringAnxietyClinical psychologyCognitive behavioral therapyCognitive processing therapyCognitionDomestic violencePsychiatryPoison controlSuicide preventionMedicinePsychological intervention

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.252
GPT teacher head0.524
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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