Bibliographic record
Abstract
The instillation of hope is a common factor in most psychotherapies. A considerable literature exists on the ethics of providing false or positively biased hope in non-psychiatric medical settings, and ethicists have generally concluded that this practice is unethical. However, the literature on the ethics of encouraging hope in psychotherapy, especially in the case of treatment-resistant mental illness, is sparse. The author explores two clinical cases with the intention of examining the nature of hope, false hope, positive illusions, and denial, as they relate to our definitions of mental health and psychotherapy. The cases highlight the ethics of balancing an acknowledgment of likely treatment futility with a desire to hope. Clinical psychological studies on depressive realism and optimistic bias indicate that some degree of positive bias, referred to by some authors as "the optimal margin of illusion," is in fact necessary to promote what we define as "good mental health;" conversely, stark realism is correlated with mild to moderate depression. An examination of the existential literature, including Ernest Becker's work, The Denial of Death, indicates that without the defense mechanism of denial, human beings tend to experience paralytic despair as a result of being fallible, mortal creatures in a frightening world. The combination of these diverse bodies of literature, along with the surprising outcomes of our case examples, leads to an unexpected conclusion: it may occasionally be ethical to encourage some degree of optimistic bias, and perhaps even positive illusion, when treating patients in psychotherapy.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| 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".