Bibliographic record
Abstract
This article is about ethics, specifically, the myriad of unethical practices characterizing recruitment for psychiatric trials. Using a case study approach, honing on recruitment material, and examining the typical, the author explores recruitment in two studies—one involving electroconvulsive therapy, the other, a psychiatric drug. The bulk of the article is on these trials. The ethical problems which surface include minimization of risk; euphemism; lack of transparency; false and misleading claims, unfair inducement; failure to mention most of the common and serious negative effects; and a predatory quality. The author also identifies some worrisome new trends. Of special interest to the humanistic counselor is the attempt to implicate people’s own counselors and therapists in recruitment. The article ends with reflections on the onus that such practices place on all practitioners striving to be ethical. The author concludes that it is critical that counselors and therapists not be complicit and beyond that they take it on themselves to confront and expose. Concrete practice suggestions include adopting an explicit policy against such referrals, alerting any clients who may be considering such trials of the danger, and countering false claims.
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 | 0.235 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.060 | 0.032 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".