ABSTRACT
Background and objectivesIn this study, we introduce the concept of benign versus harmful work stress. Our objectives are to explore how to discern benign work stress from harmful work stress and to identify the factors that promote work resilience.MethodsAn online Delphi study with three rounds, incorporating open-ended questions and statements, was administered to mental health employees and experts. Statements were rated on a 7-point scale: an interquartile deviation (IQD ≤ 1) was considered as consensus.ResultsIn the first round 20 employees and 14 experts were included, in the second round 87 employees and 35 experts, and in the third round 53 employees and nine experts. There was consensus about seven characteristics of harmful stress, eight of benign work stress, 24 individual factors that promote resilience, and eight team factors that promote resilience.ConclusionConsensus was achieved about factors relevant to benign versus harmful work stress and resilience at work. (AU)
Subject(s)
Humans , Burnout, Professional , Mental Health , Absenteeism , Anxiety , FatigueABSTRACT
BACKGROUND: Patients with mental health disorders often have difficulty perceiving associations between multiple symptoms, such as inter-relations between somatic and psychological symptoms. This difficulty may be particularly challenging in patients with complex disorders. Individual dynamic network analysis may provide novel diagnostic and treatment possibilities because it can create a starting point for a personalized approach in complex cases in tertiary mental health care expert centres, where standard protocolized interventions were insufficiently effective. AIM: To explore the possibilities provided by dynamic network technologies in the care of patients in tertiary care expert centres. METHOD: Overview of these possibilities, with a focus on somatic symptom disorder. RESULTS: Intensive longitudinal data can be obtained using a short and personalized questionnaire that is presented via a patient's smartphone a few times per day during several weeks. These data are then converted to patient-specific dynamic symptom networks using time series analysis. These networks display how variations over time in somatic and mental symptoms and other factors (such as specific situations) mutually influence each other in daily life. They also provide information about cause-effect associations. CONCLUSION: Dynamic symptom networks provide insight into the associations between symptoms and other factors and can be used to personalize treatment goals and interventions in tertiary care expert centres. Furthermore, these networks create opportunities to examine the (patient-tailored) effects of personalized interventions.
Subject(s)
Mental Disorders/therapy , Neural Networks, Computer , Patient-Centered Care , Symptom Assessment/statistics & numerical data , Humans , Mental Disorders/diagnosis , Mental Health Services , Outcome Assessment, Health Care , Psychiatric Status Rating Scales , Tertiary HealthcareABSTRACT
BACKGROUND: Although the evidence is expanding, in mental health care shared decision making (SDM) is not widely applied. Moreover, little is known about the use of routine outcome monitoring (ROM) and eHealth in SDM.
AIM: PhD research on the added value of SDM using ROM and eHealth for patients and clinicians in mental health care.
METHOD: Three studies: 1. a literature research and a cross-sectional study on decisional conflict; 2. ROM implementation research and a cluster randomised trial on shared decision making using ROM (Breakthrough program); 3. a cluster randomised trial on shared decision making during the intake (regional).
RESULTS: The interventions did not lead to reduced decisional conflict for all patient groups. Decisional conflict gives insight into the patient's perspective on the quality of the decision making process and decisions being made. Only patients with depression, who participated in the national trial, reported less decisional conflict. This trial did not show a higher level of SDM, but did show increased usage of rom in clinical practice. Although the regional trial showed no results on decisional conflict, the application of SDM and treatment outcomes improved.
CONCLUSION: SDM in mental health care needs further improvement. We recommend investigating how to support patients better, taking into account the role that suits them.
Subject(s)
Decision Making , Mental Disorders , Conflict, Psychological , Humans , Mental Disorders/therapy , Mental Health , Patient ParticipationABSTRACT
BACKGROUND: The increased attention for shared decision making (sdm) in mental health care creates a need to evaluate its application. The construct decisional conflict, which refers to the satisfaction of patients regarding both the decision making process and the decisions made, could be of added value.
AIM: Clarifying decisional conflict and reflecting on its feasibility to evaluate sdm in mental health care.
METHOD: A literature study exploring the construct of decisional conflict was conducted, followed by a translation of the results into a visual model.
RESULTS: Decisional conflict is a multi-dimensional construct and consists of factors influencing the decision making process (information, support, values clarity), level of uncertainty concerning the options and the quality of the decision making. Decisional conflict can be illustrated by using a model and assessed with the Decisional Conflict Scale.
CONCLUSION: Decisional conflict is informative and useful in the evaluation of the application of sdm and improvement of the quality of the decision making in mental health care as well. This is of importance since patients who experienced less decisional conflict are more engaged in treatment and show better clinical outcomes.
Subject(s)
Decision Making , Mental Disorders/psychology , Patient Participation , Conflict, Psychological , Decision Support Techniques , Humans , Parents/psychologyABSTRACT
OBJECTIVES: The last decades, there has been increased interest in the application of implicit pictorial tasks (e.g. Visual Probe Task (VPT), Approach Avoidance Task (AAT)) to target addictive behaviors. The present study reports on the development of implicit pictorial assessment and modification tasks aimed at targeting cognitive biases underlying motivational smoking-related cognitions (i.e. the pros and cons of smoking). METHODS: Respondents were adult daily smokers not motivated to quit smoking within six months (N = 33). A cross-sectional four-step approach using qualitative and quantitative strategies was utilized to identify and match pictures of pros and cons of smoking. RESULTS: The study resulted in 30 pro-con picture pairs matched on valence, arousal and complexity: the picture pairs were used to develop a VPT assessment and training for attentional biases and an AAT assessment and training for approach-avoidance biases. CONCLUSIONS: The developed measurement and training tasks will be used to explore and change cognitive biases regarding pros and cons of smoking. This may consequently influence the perceived pros and cons of smoking and yield positive effects with regard to the motivation to quit smoking.
ABSTRACT
BACKGROUND: The SQ4U (Stay Quit for You)-study aims to test the efficacy of two computer tailored smoking relapse prevention programs which incorporate planning strategy assignments and provide tailored feedback at multiple time points after the quit-attempt. The programs differ in the support provided after the quit-attempt. This paper aims to describe the development and design of SQ4U. METHODS/DESIGN: SQ4U-respondents were daily smokers in the 18-65 years age group, who were motivated to quit smoking and willing to make a quit-attempt within 1 month of registration. The study is a randomized controlled trial with three conditions: 1. Control group, 2. Action planning (AP) program, 3. Action planning plus (AP+) program. Respondents in the control group only filled out questionnaires. The AP program consisted of computer tailored baseline feedback based on the I-Change Model and six planning strategy assignments. The AP+ program provided the same program elements as the AP program, but was extended with tailored feedback at eleven time points after the quit-attempt. The feedback targeted (recovery) self-efficacy, planning and negative affect. There were two follow-up measurements at 6 and 12 months after baseline. User experiences and the efficacy of the programs in fostering seven-day point prevalence and continued abstinence were examined using a process evaluation and logistic regression analyses. DISCUSSION: The present paper outlines the development and design of the SQ4U-study. This study has the potential to provide a new effective computer tailored smoking relapse prevention program as well as insight into effective smoking relapse prevention strategies.
Subject(s)
Health Promotion/methods , Program Evaluation/methods , Smoking Cessation/methods , Smoking Prevention , Therapy, Computer-Assisted/methods , Adolescent , Adult , Aged , Female , Follow-Up Studies , Humans , Male , Middle Aged , Netherlands , Secondary Prevention , Surveys and Questionnaires , User-Computer Interface , Young AdultABSTRACT
OBJECTIVES: This study aims to identify the role of self-efficacy, recovery self-efficacy, and preparatory planning with regard to short-term smoking relapse. We also assessed whether the importance of these variables differed for smokers quitting individually and without help (self-quitters) and smokers quitting with the help of a smoking cessation course (group quitters). DESIGN: A longitudinal quasi-experimental study with follow-ups at 1 and 3 months after the quit attempt was conducted in order to assess the role of baseline self-efficacy, recovery self-efficacy, and preparatory planning on short-term relapse. METHODS: The recruitment included adult daily smokers (N= 121), quitting in a smoking cessation course (N= 57) and self-quitters (N= 64). Respondents received internet-based questionnaires 2 weeks before quitting (baseline) and 1 and 3 months after the quit attempt. Predictors of relapse were analysed using logistic regression analyses. RESULTS: Relapse at 1 and 3 months after the quit attempt was predicted by low levels of baseline self-efficacy. Simple slope analyses revealed that less preparatory planning significantly predicted relapse at 1 month after the quit attempt among group quitters, but not among self-quitters. Recovery self-efficacy was only predictive of relapse after 1 month when self-efficacy was excluded from the analyses. Moreover, among group quitters, the results indicated a borderline significant curved relation between recovery self-efficacy and relapse after 1 month. CONCLUSIONS: Our results suggest that more research is needed on the role of preparatory planning and recovery self-efficacy. Moreover, we recommend incorporating self-efficacy increasing techniques in relapse-prevention interventions.
Subject(s)
Self Efficacy , Smoking Prevention , Adolescent , Adult , Aged , Female , Humans , Logistic Models , Longitudinal Studies , Male , Middle Aged , Netherlands , Secondary Prevention , Smoking Cessation , Surveys and Questionnaires , Young AdultABSTRACT
No definitive picture of the factors determining smoking relapse exists, and many smoking relapse prevention programmes have only modest behavioural effects. This study aims to identify the level of consensus among and compare the opinions of two groups of experts (researchers and coaches who provide smoking cessation courses) regarding factors already studied in relation to smoking relapse, factors that have not yet been addressed and ideas on how to improve prevention programmes. A three-round Delphi method was employed. In the first round, 15 researchers completed an electronic questionnaire on factors associated with relapse. The results were used to develop a structured questionnaire for the second round, which was completed by 47 researchers and 61 coaches. The second-round results were then presented to the same experts in the third round, enabling them to re-rate their answers. Results revealed high consensus on some factors already identified as predicting relapse (e.g. self-efficacy), new factors (e.g. action planning) and several methods to improve prevention programmes. Generally speaking, the researchers and coaches provided similar suggestions. The results paint a picture of the current state of knowledge on relapse-related factors and point the way to areas for further research.
