Bibliographic record
Abstract
Adolescent Addiction: Epidemiology, Assessment, and Treatment presents a comprehensive review of information on adolescent addiction, including prevalence and co-morbidity rates, risk factors to addiction, and prevention and treatment strategies. Unlike other books that may focus on one specific addiction, this book covers a wide range of addictions in adolescents, including alcohol, cannabis, tobacco, eating, gambling, internet and video games, and sex addiction. Organized into three sections, this book begins with the classification and assessment of adolescent addiction. Section two has one chapter each on the aforementioned addictions, discussing for each the definition, epidemiology, risk factors, co-morbidity, course and outcome, and prevention and intervention. Section three discusses the assessment and treatment of co-morbid conditions in greater detail as well as the social and political implications of adolescent addictions. Intended to be of practical use to clinicians treating adolescent addiction, this book contains a wealth of information that will be of use to the researcher as well. Contributors to the book represent the US, Canada, the UK, New Zealand, and Australia. About the Editor: Cecilia A. Essau is professor of developmental psychopathology at Roehampton University in London, UK. Specializing in child and adolescent psychopathology, she has been an author or editor of 12 previous books in child psychopathology and is author of over 100 research articles and book chapters in this area. This title is comprehensive with the state-of-the-art information on important and the most common adolescent addiction. It includes easy to understand and organized chapters. It is written by international experts.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".