Bibliographic record
Abstract
Abstract Chapter 27 provides a range of resource materials. It begins with an outline of the diagnostic terms and criteria in the latest versions of two principal diagnostic and classification systems: the International Classification of Diseases, tenth revision (ICD-10) and eleventh revision (ICD-11), and the Diagnostic and Statistical Manual of Mental and Behavioural Disorders, the fifth edition (DSM-5), respectively. The chapter provides examples of common screening instruments and questionnaires including the Alcohol Use Disorders Identification Test (AUDIT) and its derivatives, the Fagerström Test for Nicotine Dependence (FTND), the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), the Internet Addiction Test (IAT), the Internet Gaming Disorder Test—Ten Items (IGDT-10), the GAMing Engagement Screener (GAMES) Test, and the Problem Gambling Severity Index (PGSI). Withdrawal monitoring scales include the Alcohol Withdrawal Scale (AWS), the Clinical Institute Withdrawal Assessment for Alcohol—Revised (CIWA-Ar), the Clinical Institute Withdrawal Assessment for Benzodiazepines (CIWA-B), the Clinical Opiate Withdrawal Scale (COWS), and the Cannabis Withdrawal Scale. Two cognitive function tests are provided: the Montreal Cognitive Evaluation (MOCA) and the third revision of the Addenbrooke’s Cognitive Examination (ACE-III). A suicide risk assessment instrument is included. The 12 Steps of Alcoholics Anonymous (AA) are listed plus a LGBTIQ+ glossary.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.801 | 0.647 |
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".