Chapter 40. Cross-Cultural Aspects of Substance-Related and Addictive Disorders
Bibliographic record
Abstract
In our ever-shrinking global village, the need for cross-cultural sensitivity and clinical competence increases along with the pace of our contact with other cultures, either through travel or via permanent resettlement. Clinical cultural competence is a lifelong journey. In this chapter, we identify the clinically relevant variables of culture and their implications for assessment and management of addictions. Most of the recent scientific literature in English addresses culture in the context of our modern multiethnic societies in the United States, Great Britain, Canada, and Australia. The definition of culture has shifted in the past several decades. Whereas the term culture has traditionally referred to an individual’s ethnicity or race, it has broadened to include such characteristics as a person’s sexual orientation. The concept of addictions has also changed. Specifically, the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association 2013) now includes behavioral addictions (gambling disorder and Internet gaming disorder) alongside traditional substance addictions such as alcohol, cannabis, and cocaine. Although the focus of this chapter remains primarily on psychoactive substances, we include behavioral addictions (gambling, gaming) when possible due to their rising relevance in psychiatry and clinical psychology (see also Chapter 42 in this volume, “Behavioral Addictive Disorders”).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".