Improving continuity of forensic mental health care
Bibliographic record
Abstract
Purpose Continuity of forensic mental health care is important in building protective structures around a patient and has been shown to decrease risks of relapse. Realising continuity can be complicated due to restrictions from finances or legislation and difficulties in collaboration between settings. In the Netherlands, several programs have been developed to improve continuity of forensic care. It is unknown whether professionals and clients are sufficiently aware of these programs. The paper aims to discuss this issue. Design/methodology/approach The experienced difficulties and needs of professionals and patients regarding continuity of forensic care were explored by means of an online survey and focus groups. The survey was completed by 318 professionals. Two focus groups with professionals (15 participants), one focus group and one interview with patients (six participants) were conducted. Findings The overall majority (85.6 percent) reported to experience problems in continuity on a frequent basis. The three main problems are: first, limited capacity for discharge from inpatient to outpatient or sheltered living; second, collaboration between forensic and regular mental health care; and, third, limited capacity for long-term inpatient care. Only a quarter of the participants knew the existing programs. Actual implementation of these programs was even lower (3.9 percent). The top three of professionals’ needs are: better collaboration; higher capacity; more knowledge about rules and regulation. Participants of the focus groups emphasized the importance of transparent communication, timely discharge planning and education. Practical implications Gathering best practices about regional collaboration networks and developing a blueprint based on the best practices could be helpful in improving collaboration between setting in the forensic field. In addition, more use of systematic discharge planning is needed to improve continuity in forensic mental health care. It is important to communicate in an honest, transparent way to clients about their forensic mental health trajectories, even if there are setbacks or delays. More emphasis needs to be placed on communicating and implementing policy programs in daily practice and more education about legislation is needed Structured evaluations of programs aiming to improve continuity of forensic mental health care are highly needed. Originality/value Policy programs hardly reach professionals. Professionals see improvements in collaboration as top priority. Patients emphasize the human approach and transparent communication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".