18 research outputs found
Models of Recovery in Mental Illness
Background. Discourse on the possibility of recovery from serious mental illness has become increasingly dominant among mental health professionals. Mental health recovery has been conceptualized variously by researchers, practitioners, policy-makers, and persons with mental illness. Several systematic reviews have synthesized the experience of recovery from the perspective of persons with mental illness, and offer different models of recovery. This proposed overview aims to summarize the methodological characteristics of systematic reviews on mental health recovery and to synthesize models of recovery from the perspective of persons with mental illness.
Design and analysis. The authors will use systematic review methods to identify and synthesize systematic reviews on the phenomenon of recovery in mental illness. A pre-specified search strategy will be used to search academic databases and libraries of the Campbell Collaboration, Cochrane Collaboration, and Joanna Briggs Institute for published and gray literature. Two authors will independently screen titles/abstracts and full texts. Authors will pilot the data extraction form before independently extracting data and appraising study quality. Reflexive thematic analysis, informed by a hermeneutic orientation towards the included texts, will be used to synthesize models of recovery presented in eligible studies.
Discussion. This overview will synthesize systematic review evidence on consumer perspectives of mental health recovery. Findings could inform future research, clinical practice, and policy by elucidating similarities and differences in recovery models across demographic or diagnostic categories and identifying how environmental, interpersonal, and intrapersonal factors contribute to recovery.
Systematic review registration: PROSPERO CRD4201914297
Firearm injury among people experiencing homelessness: Cross-sectional evidence from a national survey of United States emergency departments
OBJECTIVES: Persons experiencing homelessness (PEH) are at high risk for violent victimization. This study leverages unique data from a national study in the United States of America to provide estimates of non-fatal firearm injury among PEH and to describe the contexts related to injury, such as substance use, intent of the injury, and precipitating interpersonal factors.
STUDY DESIGN: Cross-sectional.
METHODS: Data from the 1993-2020 National Electronic Injury Surveillance System-Firearm Injury Surveillance Study (NEISS-FISS) were used to describe the context and characteristics of non-fatal firearm injury among PEH aged 16 years or older. Homeless status and substance use data were extracted from a de-identified narrative field. Estimates were weighted to account for the NEISS-FISS complex sampling design.
RESULTS: Probable homelessness was identified in 0.10% of cases (n = 3,225). Substance use was documented in 22.73% of cases. Assault comprised 82.64% of injuries. Patients were mostly male (81.38%). Missing data were common on contextual variables: verbal argument (64.62%), physical fight (54.48%) or other criminal activity (62.33%).
CONCLUSIONS: Assault is a leading cause of non-fatal firearm injury for PEH and is greater than rates of assault in non-fatal firearm injuries in the general population. Substance use was documented in nearly one quarter of patients, although this is less than expected given prior evidence. Reliance on narrative fields for key variables likely underestimates rates of PEH and substance use
Crafting, Communality, and Computing: Building on Existing Strengths To Support a Vulnerable Population
In Nepal, sex-trafficking survivors and the organizations that support them
have limited resources to assist the survivors in their on-going journey
towards reintegration. We take an asset-based approach wherein we identify and
build on the strengths possessed by such groups. In this work, we present
reflections from introducing a voice-annotated web application to a group of
survivors. The web application tapped into and built upon two elements of
pre-existing strengths possessed by the survivors -- the social bond between
them and knowledge of crafting as taught to them by the organization. Our
findings provide insight into the array of factors influencing how the
survivors act in relation to one another as they created novel use practices
and adapted the technology. Experience with the application seemed to open
knowledge of computing as a potential source of strength. Finally, we
articulate three design desiderata that could help promote communal spaces:
make activity perceptible to the group, create appropriable steps, and build in
fun choices.Comment: 14 pages, 1 figure. In Proceedings of the 2020 CHI Conference on
Human Factors in Computing Systems (CHI'20
Perspectives on aging among persons diagnosed with serious mental illness
Registration of a scoping review protoco
Prevalence and predictors of medication for opioid use disorder among reproductive-aged women
Background: Women of reproductive age would benefit from treatment of opioid use disorder (OUD) prior to pregnancy to improve maternal and infant outcomes. In this study, we aimed to identify the prevalence of medication for OUD (MOUD) and characterize correlates of MOUD receipt among 12â49-year-old women with OUD seeking treatment in publicly funded substance use disorder treatment programs at the time of their first treatment episode. Methods: This cross-sectional study explores the demographic and clinical characteristics of women of reproductive age with OUD receiving publicly funded substance use treatment services. We used data from the concatenated 2015â2021 Treatment Episode Data SetâAdmissions (TEDS-A), which documents demographic and clinical characteristics of patient admissions to publicly funded substance use treatment services in the United States. Results: In the sample of females aged 12â49 with no prior treatment admissions and primary OUD (n=325,512), 40.53% received MOUD (n=131,930), including 39.40% of non-pregnant women (n=115,315) and 52.79% of pregnant women (n=8423). Pregnant women had significantly higher odds of receiving MOUD (aOR = 2.42, 95%CI: 2.30, 2.54) compared to non-pregnant women. Non-white race, treatment setting, and treatment self-referral were also associated with higher levels of MOUD. Conclusions: We identified a significant unmet need among both pregnant and non-pregnant women with OUD seeking care in publicly funded treatment clinics. While women who are pregnant are significantly more likely to receive evidence-based treatment with MOUD, still 47.21% of pregnant women did not receive MOUD. All reproductive-aged women with OUD should be offered evidence-based treatment options, including MOUD
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Aligning implicit learning and statistical learning: Two approaches, one phenomenon
The past 15-20 years have witnessed a particularly strong
interest in our ability to rapidly extract structured
information from the environment. This fundamental
process of human cognition is widely believed to underpin
many complex behaviors â from language development and
social interaction to intuitive decision making and music
cognition â so this interest spans practically all branches of
cognitive science. Research on this topic can be found in
two related, yet traditionally distinct research strands,
namely "implicit learning" (Reber, 1967) and "statistical
learning" (Saffran, Aslin, & Newport, 1996).
Both lines of research focus on how we acquire
information from complex stimulus domains and both rely
heavily on the use of artificial systems (e.g., finite-state
grammars, pseudoword lexicons). In typical experiments,
participants are initially exposed to stimuli generated by an
artificial system and then tested to determine what they have
learned. Given these and other significant similarities,
Perruchet and Pacton (2006) argue that these distinct lines
of research actually represent two approaches to a single
phenomenon, and Conway and Christiansen (2006) propose
combining the two in name: "implicit-statistical learning".
Yet, despite frequent acknowledgements that researchers in
implicit learning and statistical learning might essentially be
looking at the same phenomenon, there is surprisingly little
alignment between the two strands.
This symposium seeks to remedy this situation by
bringing together leading researchers from both areas in
order to promote a shared understanding of research
questions and methodologies, to discuss similarities and
differences between the two approaches, and to work
towards a joint research agenda. The symposium comprises
four presentations, followed by a thematic discussion, which
provide coverage of these phenomena in terms of
development (children and adults), different language
learning tasks (sublexical phonotactics, word acquisition,
grammar learning), and their role in both production and
comprehension, each integrating multidisciplinary
perspectives. Gomez focuses on implicit-statistical learning
in early development, identifying words and grammatical
sequences and the memory systems that underlie this
learning. Monaghan and Rebuschat measure word learning
and grammar learning in adults, while varying the
knowledge that participants have of the structure they are
acquiring. Dell and Anderson demonstrate how their work
on acquisition of phonotactic constraints is exhibited in
speakersâ productions, and discuss the inter-relation in
speech between implicit and statistical learning. Finally,
Conway provides an overview of the two fields, and
proposes a novel framework that unifies implicit learning
and statistical learning