Music Making Interventions with Adults in the Forensic Setting – A Systematic Review of the Literature – Part II: Case Studies and Good vibrations
Bibliographic record
Abstract
The purpose of this systematic review of international research is to summarize the available literature on active music making interventions with adult offenders in forensic settings (i.e. forensic psychiatry or correctional facilities at different security levels). A systematic search of 13 electronic databases according to the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) statement was employed. 28 articles fitting the inclusion criteria were included in the review. The search revealed mainly qualitative and narrative reports including articles on group music therapy, educational music making, choir interventions, individual music therapy sessions and musical projects. The musical interventions are described in detail to provide therapists with ideas on how to set up session with clients who may be in this particular situation and to help them understand the possible impact of musical interventions in the forensic setting. Furthermore, implications from the current evidence and ideas for future research are discussed. Note: Due to the length of the review it is published in two subsequent issues. This is the second part of the review focusing on case studies and the Good Vibrations program. The first part of the review was published in the previous issue of Music and Medicine focusing on group interventions.
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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".