A qualitative study about explanatory models of alcohol use disorder among patients and relatives in a Ugandan mental hospital.

BACKGROUND: Alcohol use disorder (AUD) is a major clinical problem in Uganda. Explanatory models (EMs) of illness are important as they have consequences for treatment. Clinicians´ knowledge about patients´ EMs can improve understanding of the latter´s perspectives and adapting treatments. There is a lack of African studies about EMs of AUD. The aim of this study was to explore EMs for AUD among hospitalized patients and their relatives at the alcohol and drug unit (ADU) at Butabika hospital in Uganda. METHODS: An adapted version of the Explanatory Model Interview Catalogue (EMIC) was used for interviews with ten patients and five relatives to investigate how both hospitalized patients with AUD and their relatives understand the disease. Data were analysed for themes with a qualitative content analysis and support of the software program, OpenCode 4.03. RESULTS: Five major themes were identified from the patient interviews: "Context promotes AUD"; "Alcohol is part of culture"; "Spiritual causes of AUD in the community"; "Help through Western medicine and religious sources is preferred" and "Social problems and stigmatization". Six major themes identified from the interviews with relatives were: "Numerous causes of drinking alcohol"; "Devastating consequences of drinking alcohol"; "Exploiting persons with AUD"; "Others' suffering"; "Relatives struggling for help" and "Suggested solutions". CONCLUSIONS: Patients' EMs of AUD included social and spiritual explanations. Alcohol is seen as an important part of the Ugandan culture among both patients and their relatives. The results indicate it is important in clinical contexts to investigate the EMs of the patients and relatives to individually tailor treatment interventions.

Causal beliefs about mental illness: A scoping review.

Research on causal beliefs about mental illness-the beliefs people hold about what causes a particular mental illness, or mental illnesses in general-is split across a number of theories and disciplines. Although research on this subject has provided a number of insights and practical applications, the diversity of theories, terminology, and keywords makes it challenging for a new reader to gain a comprehensive understanding. We sought to address this by conducting a systematic scoping review of research on causal beliefs. This review included English-language articles from any year that mentioned causal beliefs for mental illness in their title or abstract. We identified articles in two stages. In the first stage, we used a narrow set of search terms referring specifically to causal beliefs (1227 records identified, 417 included). In the second stage, we used a comprehensive set of terms relevant to research on causal beliefs (10,418 records identified, 3838 included). We analyzed articles qualitatively, organizing them into one of five theories or categories: the common-sense model of self-regulation, explanatory models, mental health literacy, biogenetic causal beliefs, and other research on causal beliefs. We provide a comprehensive summary of these literatures in terms of their history, typical research questions and study design, findings, and practical applications. These theories differ in their theoretical orientation towards causal beliefs, research methods, findings, and applications. However, they broadly share a view of causal beliefs as multifaceted, culturally determined, and relevant for additional psychosocial variables such as mental illness stigma and help-seeking. We conclude by making recommendations for researchers, clinicians, public health messaging, and for individuals with mental illness.

"Problems you can live with" versus emergencies: how community members in rural Ethiopia contend with conditions requiring surgery.

BACKGROUND: 98% of people with surgical conditions living in low- and middle-income countries (LMICs) do not receive safe, timely and affordable surgical and anesthesia care. Research exploring barriers to receiving care has tended to be narrow in focus, often facility-based and ignoring the community beliefs, experiences and behaviours that will be an essential component of closing the gap in surgical care. Using qualitative methods, we captured diverse community perspectives in rural Ethiopia: exploring beliefs, perceptions, knowledge and experiences related to surgical conditions, with the overall aim of (re)constructing explanatory models. METHODS: Our study was nested within a community-based survey of surgical conditions conducted in the Butajira Health and Demographic Surveillance Site, southern Ethiopia, and a follow-up study of people accessing surgical care in two local hospitals. We carried out 24 semi-structured interviews. Participants were community members who needed but did/did not access surgical care, community-based healthcare workers and traditional bone-setters. Interviews were conducted in Amharic, audio-recorded, transcribed, and translated into English. We initially carried out thematic analysis and we recognized that emerging themes were aligned with Kleinman's explanatory models framework and decided to use this to guide the final stages of analysis. RESULTS: We found that community members primarily understood surgical conditions according to severity. We identified two categories: conditions you could live with and those which required urgent care, with the latter indicating a clear and direct path to surgical care whilst the former was associated with a longer, more complex and experimental pattern of help-seeking. Fear of surgery and poverty disrupted help-seeking, whilst community narratives based on individual experiences fed into the body of knowledge people used to inform decisions about care. CONCLUSIONS: We found explanatory models to be flexible, responsive to new evidence about what might work best in the context of limited community resources. Our findings have important implications for future research and policy, suggesting that community-level barriers have the potential to be responsive to carefully designed interventions which take account of local knowledge and beliefs.

Prevalence, correlates and explanatory models of cognitive deficits in patients with schizophrenia-A cross sectional study.

Background: Schizophrenia may cause significant impairment in social and economic aspects of a patient's life. Current evidence suggests that cognitive deficits may affect the functioning of a person with schizophrenia more than positive or negative symptoms. There is a lack of literature on explanatory models of cognitive deficits in schizophrenia that can influence help-seeking behavior. Objectives: This study aimed to estimate the prevalence of cognitive deficits and assess their relationship with socio-demographic and clinical characteristics among patients with schizophrenia. We also planned to explore the explanatory models of cognitive deficits in these patients. Methods: Consecutive outpatients with schizophrenia who met eligibility criteria were recruited after obtaining informed consent. The Addenbrooke's Cognitive Examination Tamil version (ACE III) and Observable Social Cognition - A Rating Scale (OSCARS) and Positive and Negative Symptom Scale (PANSS) were used to assess cognitive functioning and symptom profile, respectively. Beliefs about illness were recorded using the modified Short Explanatory Model Interview (SEMI). Socio-demographic and treatment-related details were collected with a structured proforma. Statistical analysis was done using SPSS for Windows (version 16.0.1). Results: One hundred and forty patients participated in the study. The prevalence of cognitive deficits was 75.7% using ACE-III scores, 19.3% on OSCARS, and 40% based on subjective reports. Though the majority (81.4%) of patients reported a medical explanatory model for cognitive impairment, a significant number of them (70.7%) also held non-medical models simultaneously. Conclusion: Cognitive deficits are prevalent in the majority of patients with schizophrenia. Poor test performance on cognitive testing was observed in those with a significant family history. Multiple contradictory explanatory models for the causation of cognitive deficits were reported.

Physicians' Explanatory Models of Pediatric Inflammatory Bowel Disease: A Qualitative Interview Study.

Explanatory models are culturally informed representations of illness that convey understandings of the etiology and expected course of disease. Substantial research has explored lay explanatory models, but examining physicians' clinical explanatory models can also provide insight into patients' understandings of illness because physicians are a foundational source of authoritative knowledge that shapes lay concepts of illness and disease. This study characterized the explanatory models used by pediatric gastroenterologists when explaining inflammatory bowel disease (IBD) to children. We conducted semi-structured qualitative interviews with 20 pediatric gastroenterologists across the United States about their clinical communication and explanatory models. We identified two primary explanatory models used to describe immune dysregulation in pediatric IBD: the defense and protection model, which characterizes the immune system as an army that erroneously sees the body as "non-self" and attacks it; and the switch model, which conceptualizes treatment as activating a switch that turns off a faulty immune response. We also identified two models used by some physicians to describe inflammation: the scratch and scrape model, which compares IBD inflammation to scratches or scrapes on the skin; and the bonfire model, which compares inflammation to a fire in need of extinguishing. While the use of militaristic metaphors is pervasive in medicine, describing autoimmunity as a battle against the self may lead children to perceive their body as the enemy. This may be compounded by describing the immune system as "confused" while noting its ongoing protective function. Use of these explanatory models may nevertheless improve patient disease-related knowledge.

Circular and unified analysis in network neuroscience.

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

'They tried to evil me': An explanatory model for Black Africans' mental health challenges.

This paper explores the explanatory models of mental challenges among Black Africans in England. It argues that understanding these models is critical for providing culturally appropriate care to this population. The study employed qualitative methodology, and interpretative phenomenological analysis (IPA). Twelve mental health service users who are living in England and self-identified as first or second-generation Black Africans were purposively selected. The data were gathered using face-to-face semistructured interviews. Data were manually analysed in accordance with IPA concepts of searching for common, unique and idiosyncratic themes across transcripts. The findings revealed three themes Black Africans associated to their explanatory model of mental health challenges: complexities of migration, African-centred worldview and negative life experiences. To help alleviate the Eurocentric nature of mental health practice in England, it is hoped that this explanatory model will become an integral part of mental health practice in England and around the world.

Explanatory Models of (Mental) Health Among Sub-Saharan African Migrants in Belgium: A Qualitative Study of Healthcare Professionals' Perceptions and Practices.

Culturally differing approaches to the distinction between physical and mental health contribute to cultural differences in explanatory models of what we call "mental" health in a Western context. For this reason, we use "(mental) health" in this study when referring to these models or differences in understanding. This interpretative, interview-based qualitative study focuses on Belgian mental health professionals' perceptions of the (mental) health explanatory models held by their patients of sub-Saharan African (SSA) descent. The study goals were threefold: first, to assess professionals' perceptions of the explanatory models of their patients of SSA descent; second, to examine how these perceptions influence treatment practices; and third, to investigate the role of the professionals' cultural backgrounds, comparing the results between professionals with and without an SSA background. Twenty-two in-depth interviews with mental health professionals were thematically analyzed, with ten of the participants of SSA descent. Results show that all professionals perceived differences between Western and SSA explanatory models of (mental) health. Causal beliefs were mentioned as the most important difference, including their influence on coping strategies and health-seeking behavior among patients of SSA descent. Professionals' perceptions and familiarity with SSA explanatory models of (mental) health affected their treatment practices. Language and conceptual interpretation difficulties were encountered less frequently by professionals of SSA descent. Those with a Western background applied "culturally sensitive" practices, while professionals of SSA descent implemented an integrated approach. These results contribute to ongoing discussions about what is considered "cultural competency."

"Of course, you get depression in this situation": Explanatory Models (EMs) among Afghan refugees in camps in Northern Greece.

BACKGROUND: Afghan refugees and asylum seekers constitute one of the largest groups that live in camp settings in Greece. While they experience psychological distress, they are facing significant barriers in accessing appropriate mental health care. Explanatory Models (EMs) provide a context-sensitive framework for understanding the differences between health professionals, refugees and NGOs that operate on the field. This study aims at further understanding how Afghan refugees perceive and explain depression and largely psychological distress, and how this influences their choices for coping strategies and help-seeking. METHODS: This qualitative study included six vignette-based semi-structured focus-group discussions with Afghan refugees and asylum-seekers (total N = 19, 12 female and 7 male) residing largely in camp settings in Northern Greece. The vignette describes a fictional person with symptoms of depression in line with DSM-5 and ICD-10 criteria. The interviews were recorded, transcribed, and analysed through template analysis. RESULTS: EMs for depression are explained through Pre-migration Traumatic Experiences (PMTE) and Post-migration Living Difficulties (PMLD) relating to camp settings. Female participants identified gender-based and domestic violence as contributing to psychological distress while males highlighted conflict and persecution. Life in the camp with associated inactivity, and uncertainty for the future, was perceived as a significant risk factor for psychological distress among females and males. In terms of coping strategies, females tended to focus on mobilizing collective resources within the camp (e.g. safe space for women facilitating emotional support), while males advocated for self-empowerment and solution-oriented coping. The value of engagement in peer helper-roles was highlighted. CONCLUSIONS: The results highlight the potential value of community-based psychosocial approaches to support and promote mental health within camp settings. Additionally, they may inform policies and practices regarding access to appropriate mental health care for Afghan refugees. Further research is needed to establish the efficiency of such interventions in this context.

Explanations for functional somatic symptoms across European treatment settings: A mixed methods study.

OBJECTIVE: Engaging patients in treatment for functional somatic symptoms (FSS) relies on a shared understanding of the mechanisms underlying the complaints. Despite this, little is known about the explanatory models used in daily clinical practice. We aim to examine the approaches healthcare professionals use to explain FSS across European healthcare settings. METHODS: This is an exploratory mixed methods study, combining sequential qualitative and quantitative analyses. 3 types of data were collected: a survey of Health-Care Professionals (HCPs) with special interest in FSS from 16 European countries (n = 186), Patient Education Material collected systematically from survey respondents (n = 72) and semi-structured Interviews with HCPs (n = 14). Survey results are summarized descriptively. Qualitative data was thematically coded following template analysis methods. Findings were integrated through mixed-methods triangulation. RESULTS: Five main explanatory models for FSS that are used across treatment settings and diagnostic constructs were represented in the data. The 'Multisystem Stress' Approach explains FSS through physiological stress responses within a bio-psycho-social paradigm. 'Sensitized Alarm' and 'Malfunctioning software' are both approaches derived from the neurosciences. Explanations related to 'Embodied Experience' are often used within integrated psychosomatic therapies. In the person-centred 'Symptoms' approach, HCPs aim for co-constructed, individualized explanations. These approaches, which rely on different models of mind-body-environment are complementary and are used flexibly by skilled HCPs. CONCLUSION: Taken together the explanatory models described might form the basis of a curriculum of medical explanation with the potential to equip clinicians to form more collaborative relationships with patients across healthcare.

Considering patient narrative-based and medico-scientific epistemologies in framing psychiatric care.

OBJECTIVE: Personal narratives of lived experience with psychiatric illness and distress remain central in the epistemology of mental illness. We provide a commentary on this potential bridging of patient narrative-based epistemology, and medico-scientific epistemology used by psychiatrists used for diagnosis, formulation, prognosis and treatment. CONCLUSION: Discussion and planning of psychiatric care can be framed by understanding the narrative-based epistemology of a patient's illness as highlighted by five key questions to explore the patient's illness explanatory models. We propose five key questions for the psychiatrist's complementary consideration of medico-scientific epistemology that frame conceptual models of aetiology, pathophysiology, diagnosis, formulation, prognosis and treatment, which are embedded in the predominant socio-cultural environment. These questions assist in bridging patient narrative and medico-scientific explanatory models to facilitate more effective collaborative care planning.

Explanatory models of mental illness among working professionals diagnosed with bipolar or depression in the United States.

BACKGROUND: People diagnosed with mental illness develop a variety of explanatory models of their conditions in the face of uncertainty. Explanatory models matter because they are associated with internalized stigma and illness behaviors such as treatment preferences. AIMS: This paper explores how working professionals in the United States draw on biological and/or environmental factors to explain the cause(s) of their diagnoses of bipolar or depression. METHOD: Findings are derived from an analysis of transcripts of in-depth interviews with 52 individuals from across the United States who were invited to participate in a study of working professionals diagnosed with mental illness. About 25 self-identified as having bipolar disorder and 27 as having major depression. Transcript data were analyzed following the principles of flexible coding with the goal of establishing a typology of explanatory models of self-identified bipolar versus depression. RESULTS: Six types of explanatory models emerged from the analysis. One was exclusively biological, a second was exclusively environmental, and the remaining four combined biological and environmental factors in different ways. Quotations from the interviews are provided to illustrate each type, and comparisons between types are made based on primary diagnosis (bipolar vs. depression), and self-reports of trauma and stressful experiences. CONCLUSION: Implications for the future research on explanatory models and how they may impact people who are diagnosed with a mental illness across multiple dimensions of their lives are presented.

Integrating exploration and prediction in computational psychotherapy science: proof of concept.

Introduction: Psychotherapy research has long preferred explanatory over predictive models. As a result, psychotherapy research is currently limited in the variability that can be accounted for in the process and outcome of treatment. The present study is a proof-of-concept approach to psychotherapy science that uses a datadriven approach to achieve robust predictions of the process and outcome of treatment. Methods: A trial including 65 therapeutic dyads was designed to enable an adequate level of variability in therapist characteristics, overcoming the common problem of restricted range. A mixed-model, data-driven approach with cross-validation machine learning algorithms was used to predict treatment outcome and alliance (within- and between-clients; client- and therapist-rated alliance). Results and discussion: Based on baseline predictors only, the models explained 52.8% of the variance for out-of-sample prediction in treatment outcome, and 24.1-52.8% in therapeutic alliance. The identified predictors were consistent with previous findings and point to directions for future investigation. Although limited by its sample size, this study serves as proof of the great potential of the presented approach to produce robust predictions regarding the process and outcome of treatment, offering a potential solution to problems such as p-hacking and lack of replicability. Findings should be replicated using larger samples and distinct populations and settings.

Do Gender and Country of Residence Matter? A Mixed Methods Study on Lay Causal Beliefs about PTSD.

Laypersons' causal beliefs about mental disorders can differ considerably from medical or psychosocial clinicians' models as they are shaped by social and cultural context and by personal experiences. This study aimed at identifying differences in causal beliefs about post-traumatic stress disorder (PTSD) by country and gender. A cross-sectional, vignette-based online survey was conducted with 737 participants from Germany, Greece, Ecuador, Mexico, and Russia. Participants were presented with a short unlabeled case vignette describing a person with symptoms of PTSD. Causal beliefs were assessed using an open-ended question asking for the three most likely causes. Answers were analyzed using thematic analysis. Afterwards, themes were transformed into categorical variables to analyze differences by country and by gender. Qualitative analyses revealed a wide range of different causal beliefs. Themes differed by gender, with women tending to mention more external causal beliefs. Themes also differed between the five countries but the differences between countries were more pronounced for women than for men. In conclusion, causal beliefs were multifaceted among laypersons and shared basic characteristics with empirically derived risk factors. The more pronounced differences for women suggest that potential gender effects should be considered in cross-cultural research.

Non-affective psychosis in traditional Andean culture.

We report a case of non-affective psychosis with a brief discussion of the phenomenology and its characterization and treatment by traditional Inka healers and eventually by Western-trained psychiatrists. Traditional Inka psychopathology provided empirical support for the transcultural stability of the Kraepelinian dichotomy.

Explanatory Models of Depression in a Rural Community of Coastal Karnataka, India: A Cross-Sectional Survey.

Background: Depression is a major public health problem but there is a huge treatment gap in India. Cultural beliefs influence conception of illness, personal meaning, help-seeking behaviors, and adherence to treatment. Research on explanatory models of depression attempt to explore these unique characteristics in an individual and the community. We set out to examine explanatory models of depression in a rural community of coastal Karnataka and explore the association between sociodemographic variables and explanatory models of depression. Methods: A cross-sectional household survey in the rural community of Harekala village, Mangaluru taluk, Dakshina Kannada district, Karnataka, was done using Kish tables. A total of 200 individuals were interviewed with an adaptation of the Short Explanatory Model Interview in a local language using a case vignette of depression. Results: Around 40% of the individuals perceived the problem as tension/stress/excessive worrying and did not perceive it as mental illness. A scant 10% of the participants recognized some mental illness. Around one-fifth of the individuals attributed the problem to evil spirits and black magic; female participants were more likely to endorse consulting a doctor (P = 0.003**) or a psychiatrist (P = 0.012*). In addition, participants belonging to Islam were less likely to consult a doctor (P = 0.028*) and psychiatrist (P = 0.021*). Also, participants belonging to lower social class were less likely to endorse psychiatric consultation (P = 0.018*). Conclusions: A vast majority of the study subjects failed to identify depression as an illness or acknowledge biomedical causation. Gender, religion, and socioeconomic class may influence help-seeking behavior.

Contribution of Two-Dose Vaccination Toward the Reduction of COVID-19 Cases, ICU Hospitalizations and Deaths in Chile Assessed Through Explanatory Generalized Additive Models for Location, Scale, and Shape.

Objectives: To assess the impact of the initial two-dose-schedule mass vaccination campaign in Chile toward reducing adverse epidemiological outcomes due to SARS-CoV-2 infection. Methods: Publicly available epidemiological data ranging from 3 February 2021 to 30 September 2021 were used to construct GAMLSS models that explain the beneficial effect of up to two doses of vaccination on the following COVID-19-related outcomes: new cases per day, daily active cases, daily occupied ICU beds and daily deaths. Results: Administered first and second vaccine doses, and the statistical interaction between the two, are strong, statistically significant predictors for COVID-19-related new cases per day (R2 = 0.847), daily active cases (R2 = 0.903), ICU hospitalizations (R2 = 0.767), and deaths (R2 = 0.827). Conclusion: Our models stress the importance of completing vaccination schedules to reduce the adverse outcomes during the pandemic. Future work will continue to assess the influence of vaccines, including booster doses, as the pandemic progresses, and new variants emerge. Policy Implications: This work highlights the importance of attaining full (two-dose) vaccination status and reinforces the notion that a second dose provides increased non-additive protection. The trends we observed may also support the inclusion of booster doses in vaccination plans. These insights could contribute to guiding other countries in their vaccination campaigns.

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic.

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Assessment of Malingering among Servicemembers with a Focused Examination of Explanatory Models.

Umbrasas briefly mentioned established models of malingering that sought to understand the driving motivations for feigning mental disorders. He used these models as a point of departure to consider the unique experiences and enduring challenges of active and retired servicemembers. For military malingering, he identified acute distress malingering and disability malingering with the former occurring within the first five years and the latter after a military career had been established. To provide a strong foundation, this commentary revisits three explanatory models of malingering (i.e., pathogenic, criminological, and adaptational) that have been empirically tested. Of these, the adaptational model appears the best suited in the military context to explain nongenuine responding because most mandatory evaluations carry highly negative consequences, such as seriously damaging future careers in the armed forces. Most examinees would be seen as denying and defensive, however, the polar opposite of malingering. When symptoms are eventually reported, Umbrasas acknowledges the temptation to consider this atypical presentation as possible evidence of malingering. We concur with Umbrasas's conclusion that such an extrapolation would be generally unwarranted. In summary, the overriding goal of this commentary is to understand Umbrasas's thought-provoking contributions to military malingering in the larger landscape of explanatory models of malingering.