Tattoo Removal in Forensic Mental Health Settings: A Case for Advocacy
Bibliographic record
Abstract
The significance of visible tattoos in psychiatric patients should be explored with them. This can reveal important information about their psychological well-being. Facial tattoos can impact the individual's appearance and self-esteem, especially if the tattoo(s) were acquired during an episode of mental illness. It serves as a constant reminder of the time when the person was unwell and can hinder progress and rehabilitation. Removal of such tattoo(s) can aid in the recovery of these patients. While tattoo removal services are available, patients with chronic and severe mental health problems often lack the funds for tattoo removal. Similarly, patients within forensic mental health settings may have additional restrictions on their movement, which serve as a barrier to accessing treatment. We would like to discuss the case of a young male patient who is undergoing tattoo removal with significant improvement in self-esteem and call for advocacy in this regard.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".