Bibliographic record
Abstract
To provide effective psychotherapy for culturally different patients, therapists need to attain cultural competence, which can be divided broadly into the 2 intersecting dimensions of generic and specific cultural competencies. Generic cultural competence includes the knowledge and skill set necessary to work effectively in any cross-cultural therapeutic encounter. For each phase of psychotherapy--preengagement, engagement, assessment and feedback, treatment, and termination--we discuss clinically relevant generic cultural issues under the following headings: therapist, patient, family or group, and technique. Specific cultural competence enables therapists to work effectively with a specific ethnocultural community and also affects each phase of psychotherapy. A comprehensive assessment and treatment approach is required to consider the specific effects of culture on the patient. Cultural analysis (CA) elaborates the DSM-IV cultural formulation, tailoring it for psychotherapy; it is a clinical tool developed to help therapists systematically review and generate hypotheses regarding cultural influences on the patient's psychological world. CA examines issues under 3 domains: self, relations, and treatment. We present a case to illustrate the influence of culture on patient presentation, diagnosis, CA, and psychotherapeutic treatment. Successful therapy requires therapists to employ culturally appropriate treatment goals, process, and content. The case also demonstrates various techniques with reference to culture, including countercultural, cultural reinforcing, or culturally congruent strategies and the use of contradictory cultural beliefs. In summary, developing both generic and specific cultural competencies will enhance clinician effectiveness in psychotherapy, as well as in other cross-cultural therapeutic encounters.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".