The Great Pretender
Susannah Cahalan
From "one of America's most courageous young journalists" (NPR) comes a propulsive narrative history investigating the fifty-year-old mystery behind a dramatic experiment that changed the course of modern medicine. For centuries, doctors have struggled to define mental illness--how do you diagnose it, how do you treat it, how do you even know what *it* is? In search of an answer, in the 1970s a Stanford psychologist named David Rosenhan and seven other people--sane, normal, well-adjusted members of society--went undercover into asylums around America to test the legitimacy of psychiatry's labels. Forced to remain inside until they'd "proven" themselves sane, all eight emerged with alarming diagnoses and even more troubling stories of their treatment. Rosenhan's watershed study broke open the field of psychiatry, hastening the closing of institutions and changing mental health diagnosis forever. But, as Cahalan's explosive new research shows, very little in this saga is exactly as it seems. What really happened behind those closed asylum doors, and what does it mean for our understanding of mental illness today? "Breathtaking! Cahalan's brilliant, timely, and important book reshaped my understanding of mental health, psychiatric hospitals, and the history of scientific research.
Award History
0 wins · 1 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| Royal Society Science Book PrizeMajor | 2020 | Shortlist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
- David Rosenhan78%
02 Central places
None extracted with sufficient confidence.
03 Suggested argument
No argument inferred with sufficient confidence.
04 Reading orientation
35 / 100 academicSerious trade
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 72%.