Reply to ‘Civilizing the “Barbarian”: a critical analysis of behaviour modification programmes in forensic psychiatry settings’
Bibliographic record
Abstract
bowler n. & williams m. (2011) Journal of Nursing Management 19, 302–304Reply to ‘Civilizing the “Barbarian”: a critical analysis of behaviour modification programmes in forensic psychiatry settings’ (Holmes & Murray 2011) Aim To consider ethical propositions relating to nursing in UK forensic settings. Background A previous paper considered behavioural programmes with a Canadian forensic population. Method Some literature and personal reflections are presented. Results Whilst some similarities with the nature of Canadian forensic settings are identified, the UK is developing its’ own cognitive-behavioural tradition of working. Conclusions The skills necessary for working with forensic patients are a development of a wider mental health nursing and therapy skill-set. Implications for nursing management Nurse managers within forensic services need to be clear about the values of their services and ensure that therapeutic approaches are consistent with these values. Managers must consider how to support nurses acquire the requisite skill-set.
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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.043 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".