Affective Forecasting and Its Implications for Medical Ethics
Bibliographic record
Abstract
Through a number of studies recently published in the psychology literature, T.D. Wilson, D.T. Gilbert, and others have demonstrated that our judgments about what our future mental states will be are contaminated by various distortions. Their studies distinguish a variety of different distortions, but they refer to them all with the generic term “affective forecasting.” The findings of their studies on normal volunteers are remarkably robust and, therefore, demonstrate that we are all vulnerable to the distortions of affective forecasting.We are grateful for the questions and useful comments we received from our audience when material from this paper was originally presented. The insightful remarks helped us to appreciate what we needed to explain further and to see how our ideas applied to additional domains of medicine: Rhodes R. Affective forecasting and the implications for medical practice. Presented at Medicine Grand Rounds, North General Hospital, New York, Apr 19, 2006. Rhodes R, Strain JJ. Affective forecasting and its implications for medical ethics. Presented at Oxford-Mount Sinai Consortium on Bioethics, St. Thomas's Hospital, King's College London, Apr 24, 2006. Rhodes R. Affective forecasting and its implications for medical ethics near the end of life. Presented at Responding to End-of-Life Decisions: Perspectives from Medicine, Law, and Ethics, International Academy of Law and Mental Health, University of Montreal, May 5, 2006 and at the David Thomasma International Bioethics Retreat, Pellegrue, France, Jun 15, 2006.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".