Book Review: Eating Disorders Cognitive Behavior Therapy and Eating Disorders
Bibliographic record
Abstract
This excellent, well-written book is different from the other texts that review depression in older adults.In particular, there is a focus on real-life experiences and different perspectives of front-line health care providers.I would encourage clinicians and hospital libraries to obtain a copy of this book.This book is easy to read and to understand.The educational objectives outlined in this book are met.The source of the primary data is collected from primary care physicians.The perspective of clinicians from various disciplines working in concert to manage depressed elders is explored.This captures the real day-to-day situations that are dealt with in everyday practice.Many texts describe the importance of a collaborative approach to treatment.Yet the descriptions are usually through the eyes of one discipline.The editors of this text provide the reader with the views of multiple practitioners reflecting on the diagnosis and treatment of an individual patient.A unique contribution of this text is its multicultural perspective.This perspective emerges in at least 2 ways.First, the authors describe the diversity of patients whom the primary care physician encounters in the United Kingdom.Second, they provide a cross-cultural reflection on a case of old-age depression.The actual text of the responses from clinicians around the world appears in the Appendix of Chapter 6.I found this quite interesting to read.This material captures how patients are influenced by their own cultural background.What is also highlighted is how clinicians view their patients differently given their own cultural background, even though many approaches to therapy are virtually universal.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.019 |
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".