Placebos in medicine: from conceptualizations in medical school to integration in clinical practice
Bibliographic record
Abstract
Placebo effects constitute context-dependent medical phenomena, and exemplify the link between psychosocial factors and physiological processes.By surveying medical students (Study 1) and practicing physicians (Study 2), the present thesis explores the knowledge and attitudes towards the role of placebos, and their related effects, in the clinic.In addition, this thesis addresses the ethics of deception in clinical practice (Commentary Response 1).Findings from Study 1 demonstrate that medical students are unaware of many non-drug effects that influence response to treatment, and yet, approximately 40% reported that they would prescribe a placebo in clinical practice.Such placebo science knowledge is critical, moreover, considering recent reports documenting the widespread use of placebos in clinical practice.These findings may foster informed discussions regarding the inclusion of placebo science in medical curricula.Study 2 reveals substantive differences among subspecialties of medicine, specifically between psychiatrists and nonpsychiatrists, on the issue of placebos.Moreover, approximately 20% of physicians in Canada reported that they have prescribed or administered a placebo in the course of routine clinical practice.Such findings have implications for formal policy regarding the use of placebos in the clinic.Altogether, these findings underscore the crucial role placebos and non-drug effects have within the medical community, and emphasize the value of further exploration into the intricacies of placebo knowledge, patterns of use, and efficacies within various practices.
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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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.106 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".