Book Review: AIDS: The Psychiatry of AIDS: A Guide to Diagnosis and Treatment
Bibliographic record
Abstract
AIDS The Psychiatry of AIDS: A Guide to Diagnosis and Treatment Glenn J Treisman, Andrew F Angelino. Baltimore (MD): Johns Hopkins University Press; 2004. 217 p. US$19.95. Reviewer Rating: Excellent This book's author, Dr Glenn Treisman, has led the Psychiatric Services for the Johns Hopkins HIV/AIDS Care Program for more than 12 years, while its coauthor, Dr Andrew Angelino, has broad experience in the field. The preface, by Dr Treisman, sets the tone. He acknowledges the work of his colleagues with profound understanding, stating, for example, are the trenches . . . these clinicians have waded into the mud of psychiatric illness and dragged patients out one at a time (p xii). The book is upbeat and optimistic, yet realistic and deeply human. There are 9 chapters: AIDS Psychiatry? and Major Depression, Other Psychiatric Diseases in the HIV Clinic (including AIDS dementia), Personality in the HIV Clinic, Substance Abuse and HIV, Sexual Problems and HIV, Life Story Problems in the HIV Clinic, Special Problems: Hepatitis C and Adherence, and How to Fight AIDS. These are interspersed with case studies that illustrate and bring to life the didactic material. The authors approach the topic of each chapter by focusing on how clinicians can understand the psychiatric condition in a way that opens new therapeutic possibilities rather than one that fosters pessimism. The paradigms discussed are refreshing and enlightening. For example, in discussing substance abuse, the authors explore the inherent reinforcing properties of the substance but also raise the questions of why, if these substances are reinforcing, does not everyone who experiments with them become addicted? Why do some people not become addicted? Without specifically promoting gentle (and sometimes not so gentle) confrontation, this is clearly the favoured approach in the context of a supportive relationship. While this straight-talk style may not suit the personality of every clinician, the examples do debunk the myth that confrontation will inevitably hurt the therapeutic alliance. For example, consider the following exchange: did you miss your last appointment here at the clinic? It was snowing. Would you have gone out to 'cop' if you were using? Of course. Well, you have to be willing to go out in worse weather to get sober than you would to 'cop,' because it is harder to get sober than to stay an addict (p xiii). …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.090 | 0.079 |
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".