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Background: Recovery orientation is a movement in mental health practice. Although general mental health services have taken the lead in promoting recovery, forensic psychiatric systems have lagged behind because of the need to reconcile recovery principles with the complexities of legal mandates. Advocating recovery and making systemic changes can be challenging because they require seeking a balance between the competing duties to the patient and the public. This paper used a logic model framework to demonstrate a cohabitation program that placed a woman and her newborn infant in a secure forensic rehabilitation unit, and analyzed the key assumptions of recovery upon which it was based.
Methods: This was a qualitative program evaluation. Data collection involved individual interviews with the woman, the infant's father, five primary healthcare providers, and five system administrators, and 11 focus groups with unit staff and other patients. Content analysis was used to guide the data analysis and develop the critical components of the program logic model.
Results: A logic model that consists of input (team building, program planning, staff and patient preparation, resource management), output (logistic activities, risk management, mental healthcare, staff/other patient support, discharge preparation), and outcome (individual, provider, system, and society) components was developed.
Conclusions: This study demonstrates a recovery-oriented program for a woman cohabitating with her baby in a secure forensic psychiatric rehabilitation unit. The logic model provided a comprehensive understanding of the way the recovery principles, such as shared decision-making, positive risk-taking, informed choices, and relational security, were implemented.
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http://dx.doi.org/10.3390/ijerph19010009 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
International Communication College, Jilin International Studies University, Changchun, Jilin, China.
Background: Conventional automated writing evaluation systems typically provide insufficient support for students with special needs, especially in tonal language acquisition such as Chinese, primarily because of rigid feedback mechanisms and limited customisation.
Objective: This research develops context-aware Hierarchical AI Tutor for Writing Enhancement(CHATWELL), an intelligent tutoring platform that incorporates optimised large language models to deliver instantaneous, customised, and multi-dimensional writing assistance for Chinese language learners, with special consideration for those with cognitive learning barriers.
Methods: CHATWELL employs a hierarchical AI framework with a four-tier feedback mechanism designed to accommodate diverse learning needs.
PLoS One
September 2025
Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia.
In the paper we consider the well-known Influence Maximization (IM) and Target Set Selection (TSS) problems for Boolean networks under Deterministic Linear Threshold Model (DLTM). The main novelty of our paper is that we state these problems in the context of pseudo-Boolean optimization and solve them using evolutionary algorithms in combination with the known greedy heuristic. We also propose a new variant of (1 + 1)-Evolutionary Algorithm, which is designed to optimize a fitness function on the subset of the Boolean hypercube comprised of vectors of a fixed Hamming weight.
View Article and Find Full Text PDFChem Sci
August 2025
Shanghai Key Laboratory of Functional Materials Chemistry, Institute of Fine Chemicals, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology Shanghai 200237 China
Real-time monitoring of senescent cells is of great significance for understanding and intervening in aging. Since overexpression of endogenous β-galactosidase (β-gal) is not unique to senescent cells, probes relying solely on β-gal activity could yield inaccurate senescent cell detection. Herein, we designed a dual-mode sequential response AND logic NIR probe MFB-βgal, which contains a β-gal-cleavable unit and a morpholine unit, serving as an enzymatic activity trigger and a lysosomal targeting moiety, respectively.
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