Review of Handbook of Clinical Health Psychology: Volume 1, Medical Disorders and Behavioral Applications.
Bibliographic record
Abstract
SUZANNE BENNETT JOHNSON, NATHAN W. PERRY, JR., and RONALD H. ROZENSKY (Volume Eds.) Handbook of Clinical Health Psychology: Volume 1, Medical Disorders and Behavioral Applications Washington DC: American Psychological Association, 2002, 654 pages. (ISBN 1-55798-909-5, US$69.95 Hardcover) The goal of the three volume Handbook of Clinical Health Psychology, published by The American Psychological Association, is to describe in detail health psychology's contribution to scientific knowledge and improved health care delivery. The information to be covered makes this series of three handbooks the first comprehensive effort to characterize the field of health psychology. As noted in the series introduction, it does this by describing health psychology's scientific basis, delineating specific techniques and evaluation procedures, and by demonstrating applications of health psychology to the full range of medical diagnoses. This is extremely worthwhile, given how slowly physicians, the patient population, and third-party payers have been to recognize the positive impact that psychological interventions have on health care delivery. This handbook's publication is particularly timely, given the recent reduction of psychological services in many Canadian hospitals. The first volume in this series is entitled: Medical Disorders and Behavioral Applications. Forthcoming titles in the series are: Volume 2: Disorders of Behavior and Health, and Volume 3: Models and Perspectives in Clinical Health Psychology. Volume 1 focuses on health psychology's contributions to the management of specific diseases and disorders. The volume is organized around the International Classification of Diseases, Ninth Revision (ICD-9, 1998), a coding system used in the U.S. and commonly used worldwide. This volume is comprised of 17 chapters exactly paralleling the 17 categories into which the ICD-9 organizes diseases and disorders. In each of these chapters, chapter authors, typically health psychologists with experience in one or more of the chapter's disorder, first briefly describe the diseases and disorders that fall within their specific ICD-9 disease category (e.g., Chapter 8, Diseases of the Respiratory System). Next, the authors provide some epidemiological data relevant to these diseases and disorders and highlight health psychology's contributions to these conditions. Finally, chapter authors conclude by commenting on areas in which health psychology may yet have made minimal impact and suggest opportunities for new research and applications. The volume does a good job in providing a systematic overview of all the ICD-9 disease categories, with the disease and disorder descriptions included in each chapter being particularly comprehensive. However, adopting the ICD-9 organizational approach, as this volume does, has some drawbacks. The first being the ICD-9's rather rigid mind-body dualism, that may be contrary to the biopsychosocial model which underlies the health psychology approach. For example, the ICD-9 classification system deems that physical diseases seen as being in some way psychogenic in origin are to be classified under Mental Disorders whereas if they are seen as organic in origin they may be classified under their specific disease category and never the twain shall meet. …
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.044 |
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".