Therapeutic doublethink: Making a case for the acceptability of unrealistic optimism in patients at end-of-life
Bibliographic record
Abstract
George Orwell coined the term "doublethink" to describe the concept of "holding two contradictory beliefs in one's mind simultaneously, and accepting both of them." Patients at end-of-life may be using “Therapeutic Doublethink” as a psychological mechanism to cope with the cognitive dissonance resulting from accepting a terminal diagnosis and desiring curative therapy. To develop the concept of Therapeutic Doublethink, we explored the literature on Unrealistic Optimism and decision-making, then grounded them in existing psychological and neurobiological concepts. Functional neuroimaging studies can help to explain the biological mechanisms that permit Unrealistic Optimism and Therapeutic Doublethink to exist, including a failure to track less desirable information and a focus on outcomes without considering the actions required to achieve them. These findings help us appreciate the challenges patients face when trying to make “rational” treatment decisions. They also raise questions about the effectiveness and potential harm of truth-telling, which is a common approach used by physicians who are confronted by Unrealistic Optimism. In some cases, physicians may be able to use Therapeutic Doublethink to harness the beneficial aspects of Unrealistic Optimism while simultaneously helping patients accept the terminal nature of their condition. This approach may be conceptually and ethically challenging for many physicians, but has important implications for improving patient care.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".