Physician Attitudes towards Pharmacological Cognitive Enhancement: Safety Concerns Are Paramount
Bibliographic record
Abstract
The ethical dimensions of pharmacological cognitive enhancement have been widely discussed in academic circles and the popular media, but missing from the conversation have been the perspectives of physicians - key decision makers in the adoption of new technologies into medical practice. We queried primary care physicians in major urban centers in Canada and the United States with the aim of understanding their attitudes towards cognitive enhancement. Our primary hypothesis was that physicians would be more comfortable prescribing cognitive enhancers to older patients than to young adults. Physicians were presented with a hypothetical pharmaceutical cognitive enhancer that had been approved by the regulatory authorities for use in healthy adults, and was characterized as being safe, effective, and without significant adverse side effects. Respondents overwhelmingly reported increasing comfort with prescribing cognitive enhancers as the patient age increased from 25 to 65. When asked about their comfort with prescribing extant drugs that might be considered enhancements (sildenafil, modafinil, and methylphenidate) or our hypothetical cognitive enhancer to a normal, healthy 40 year old, physicians were more comfortable prescribing sildenafil than any of the other three agents. When queried as to the reasons they answered as they did, the most prominent concerns physicians expressed were issues of safety that were not offset by the benefit afforded the individual, even in the face of explicit safety claims. Moreover, many physicians indicated that they viewed safety claims with considerable skepticism. It has become routine for safety to be raised and summarily dismissed as an issue in the debate over pharmacological cognitive enhancement; the observation that physicians were so skeptical in the face of explicit safety claims suggests that such a conclusion may be premature. Thus, physician attitudes suggest that greater weight be placed upon the balance between safety and benefit in consideration of pharmacological cognitive enhancement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".