Matching intensity to people’s needs and circumstances requires rethinking traditional care pathways and aligning them with personal recovery goals by reducing dependency on professionals where possible. Social support is recognized as a crucial factor in mental healthcare usage, and this study examined its role in how the intensity of mental healthcare is adjusted. We analyzed data from a four-year naturalistic cohort study of Dutch individuals with mental disorders (n = 293). The Social Network Map was employed to assess social support. The Mental Healthcare Intensity Scale measured healthcare usage. A linear mixed-effects model was utilized to explore the relationship between social support and healthcare intensity, while controlling for demographic variables and healthcare usage one year earlier. Previous levels of social support significantly influenced the intensity of mental healthcare. Participants with little support saw minimal changes in the intensity of the care they received. However, those with more support experienced dynamic changes, which differed significantly from those in the previous year. This effect was both positive and negative. It showed that social support acts as a catalyst by either escalating or deescalating the intensity of care needed. Social support plays a vital role in adjustments in the intensity of mental healthcare. This underscores the need for mental health professionals to incorporate social support assessments into clinical evaluations and be aware of this effect when deciding on the intensity of mental healthcare needed for people with a mental illness.

Purpose:The Treatment Expectation Questionnaire (TEX-Q) is a generic, multidimensional scale that measures patients' expectations of medical and psychological treatments. Currently, it is available in English and German only. This study aims to translate the TEX-Q into Dutch and evaluate its psychometric properties. Patients and Methods:The TEX-Q was translated into Dutch following international guidelines for cross-cultural adaptation of self-report measures. The Dutch version was tested in 163 gynaecological outpatients starting new treatments, a group suitable for validation due to their diverse treatment experiences. Test-retest reliability was assessed in a sample of 25 gynaecological outpatients. We examined data completeness (ie, no missing data), score distributions, internal consistency, construct validity, confirmatory factor analysis (CFA), and test-retest reliability. Convergent validity was tested through correlations with Credibility/Expectancy Questionnaire (CEQ) and a single item from Brief Illness Perception Questionnaire (B-IPQ). Discriminant validity was assessed using Life Orientation Test (LOT-R), General Self-Efficacy Scale (GSES), Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9). Results:Data completeness was 89%. Internal consistency, measured by Cronbach's α, was good for most subscales, with values mostly above 0.75, except for the 'Process" subscale (Cronbach's α = 0.55). The mean TEX-Q score showed moderate to strong correlations with CEQ and the B-IPQ item (r = 0.29-0.55). Correlations with discriminant measures like LOT-R, GSES, GAD-7 and PHQ-9 were low (r = 0.22, 0.23, -0.17, -0.14 respectively). CFA revealed an acceptable six-factor model. Factor loadings were high (minimum of 0.76). The test-retest reliability was moderate for the mean TEX-Q score (ICC = 0.72). Conclusion:The Dutch version of the TEX-Q demonstrates acceptable validity and reliability, making it suitable for research and clinical practice in gynaecology. Further validation across diverse clinical populations is recommended.

Feedback-Informed Group Treatment (FIGT) shows promise for improving outcomes, but results are mixed. The aim was investigating the feasibility, acceptability and effects of renewed FIGT on clinical outcomes and therapy processes. In a quasi-experimental pilot study, 65 patients with anxiety or depressive disorders and 15 therapists of interpersonal psychotherapy or cognitive behavioural therapy groups using renewed FIGT were included. Renewed FIGT contained three additions compared to the previous tool: (1) personalized goals along with the Outcome Questionnaire-45 (OQ-45), (2) therapists’ training, coaching and intervision, and (3) instructions to actively use feedback in the group. Data on feasibility, acceptability, outcomes and process factors were analysed and compared with those of historical cohorts using only OQ-45 feedback or no feedback, using descriptive, multilevel and covariance statistical analyses. Feasibility was mostly improved, with patients experiencing more feedback discussions and better usability compared to only OQ-45 feedback. At least two thirds of the patients and therapists give preference to using feedback in the future. At the end of the study, therapists were less convinced that the OQ-45 and goals were able to detect change. Renewed FIGT did not improve effectiveness on clinical outcomes. Compared to no feedback, patients experienced more cohesion, engagement and less avoidance, but improved less on depressive symptoms. Even when renewed FIGT is more feasible and usable than only OQ-45 feedback and associated with more cohesiveness and engagement, it may not automatically lead to improved effectiveness on clinical outcomes in short-term group therapy. Implications and future directions are described.

Disruptive behavior of patients in acute psychiatric care is a problem for both patients and staff. Preventing a patient’s impending disruption requires recognizing and understanding early signals. There are indications that a change in a patient’s global functioning may be such a signal. The global functioning of patients is a multidimensional view on their functioning. It captures a patient’s psychological symptoms, social skills, symptoms of violence, and activities in daily living. The aim of this study was to gain insight into the predictive value of global functioning on the risk of disruptive behavior of patients in acute psychiatric care. Also assessed was the time elapsed between the change in global functioning and a patient’s disruptive behavior, which is necessary to know for purposes of early intervention. In a longitudinal retrospective study, we used daily measurements with the Brøset Violence Checklist (BVC) and the Kennedy Axis V (K-As) of each patient admitted to two acute psychiatric units over a period of six years. Data from 931 patients for the first 28 days after their admission were used for survival analysis and cox regression analysis. Disruptive behavior was mostly observed during the first days of hospitalization. Global functioning predicted disruptive behavior from the very first day of hospitalization. A cut-off score of 48 or lower on the K-As on the first admission day predicted a higher risk of disruptive behavior. If functioning remained poor or deteriorated substantially over three days, this was an additional signal of increased risk of disruptive behavior. Improvement in global functioning was associated with a decreased risk of disruptive behavior. More attention is needed for early interventions on global functioning to prevent disruptive behavior.

ObjectiveThe Mental Health Self-Direction Scale (MHSD) measures the extent to which clients are able to resolve mental problems by themselves. Because this scale had not yet been evaluated, the aims of this paper were (a) to evaluate and improve the MHSD and (b) to explore the sensitivity to change of the improved scale.MethodThe MHSD was evaluated and improved by means of confirmatory factor analyses of data from one longitudinal and two cross-sectional outpatient sample. Inconsistent items were removed in a stepwise fashion. Subsequently, the scale's sensitivity to change was explored in the longitudinal sample by using latent growth curve models.ResultsThe original 31-item scale was reduced to a more stable version with 19 items that yielded four factors named: actorship, demoralization, commitment, and understanding. Throughout clients' treatment, actorship and understanding tended to increase; demoralization tended to decrease; and commitment remained consistently high.ConclusionsThe abridged MHSD scale is stable and sensitive to change. It measures the extent to which clients experience and develop self-direction throughout their treatment. With the use of the new MHSD scale, new views on mental health that emphasize clients' ability to actively engage and cope with health-challenges can be incorporated into clinical treatment.

Het begrip ‘passende zorg’ vormt de kern van het Integraal Zorgakkoord. Voor veel doelgroepen is onduidelijk wat dit concreet inhoudt, zo ook voor de groep cliënten met stabiele chronische problematiek. Met het POS!TION consortium (een multidisciplinaire samenwerking van professionals en/of onderzoekers werkzaam bij ggz-aanbieders, kennisorganisaties en cliëntenorganisaties)1 is onderzocht hoe de zorg voor hen er momenteel uitziet en wat volgens professionals en cliënten belangrijke knelpunten zijn. Aan de hand van de resultaten uit dit onderzoek hebben de auteurs een onderzoeksagenda opgesteld. De antwoorden op deze onderzoeksvragen zouden op termijn moeten leiden tot meer passende en wetenschappelijk onderbouwde zorg.

A significant proportion of patients with a personality disorder do not benefit from treatment. Monitoring treatment progress can help adjust ineffective treatments. This study examined whether early changes in symptoms and personality dysfunction during the first phase of therapy could predict treatment outcomes. Data from 841 patients who received specialized treatment for personality disorders were analyzed. The study focused on whether changes in the Outcome Questionnaire-45.2 (OQ-45.2) symptom distress scale (SD), the General Assessment of Personality Disorder (GAPD), and Severity Indices of Personality Problems (SIPP) in the early phase of therapy predicted post-treatment personality dysfunction, as measured by the SIPP and GAPD. Early changes within a specific SIPP domain were the strongest predictors of post-treatment outcomes in that same domain. Early changes in symptoms significantly predicted outcomes in Self-Control, Relational Functioning, and Identity Integration, while the GAPD predicted outcomes in Self-Control and Social Attunement on the SIPP. For the GAPD, early changes on the GAPD itself, followed by early changes on the OQ-45 SD and the SIPP domain Social Attunement, were significant predictors. Thus, when it comes to personality dysfunction, early changes in a specific domain or measure are the best predictors of outcomes in that same domain. While the OQ-45 predicted some aspects of personality dysfunction, it should not replace disorder-specific measures. Additionally, the SIPP domains and the GAPD should not be used interchangeably to predict each other. In sum, considering these factors, monitoring early change can be useful in assessing progress in the treatment of patients with personality disorders.

BACKGROUND:Previous studies have shown that being employed is associated not only with patients' health but also with the outcome of their treatment for severe mental illness. This study examined what influence employment had on improvements in mental health and functioning among patients with common mental disorders who received brief treatment and how patients' diagnosis, environmental and individual factors moderated the association between being employed and treatment outcome.METHODS:The study used naturalistic data from a cohort of patients in a large mental health franchise in the Netherlands. The data were obtained from electronic registration systems, intake questionnaires and Routine Outcome Monitoring (ROM). The International Classification of Functioning, Disability and Health (ICF) framework was used to identify potential subgroups of patients. Logistic regression models were used to analyze the relationship between employment status and treatment outcome and to determine how the relationship differed among ICF subgroups of patients.RESULTS:A strong relationship was found between employment status and the outcome of brief therapy for patients with common mental disorders. After potential confounding variables had been controlled, patients who were employed were 54% more likely to recover compared to unemployed patients. Two significant interactions were identified. Among patients who were 60 years of age or younger, being employed was positively related to recovery, but this relationship disappeared in patients older than 60 years. Second, among patients in all living situations there was a positive effect of being employed on recovery, but this effect did not occur among children (18+) who were living with a single parent.CONCLUSIONS:Being employed was positively associated with treatment outcome among both people with a severe mental illness and those with a common mental disorder (CMD). The main strength of this study was its use of a large dataset from a nationwide franchised company. Attention to work is important not only for people with a severe mental illness, but also for people with a CMD. This means that in addition to re-integration methods that focus on people with a severe mental illness, more interventions are needed for people with a CMD.

Background: Predicting which treatment will work for which patient in mental health care remains a challenge.Objective: The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models.Methods: Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites.Results: The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80.Conclusions: Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

There are considerable differences among mental healthcare services, and especially in developed countries there are a substantial number of different services available. The intensity of mental healthcare has been an important variable in research studies (e.g. cohort studies or randomized controlled trials), yet it is difficult to measure or quantify, in part due to the fact that the intensity of mental healthcare results from a combination of several factors of a mental health service. In this article we describe the development of an instrument to measure the intensity of mental healthcare that is easy and fast to use in repeated measurements. The Mental Healthcare Intensity Scale was developed in four stages. First, categories of care were formulated by using focus group interviews. Second, the fit among the categories was improved, and the results were discussed with a sample of the focus group participants. Third, the categories of care were ranked using the Segmented String Relative Rankings algorithm. Finally, the Mental Healthcare Intensity Scale was validated as a coherent classification instrument. 15 categories of care were formulated and were ranked on each of 12 different intensities of care. The Mental Healthcare Intensity Scale is a versatile questionnaire that takes 2-to-3 min to complete and yields a single variable that can be used in statistical analysis. The Mental Healthcare Intensity Scale is an instrument that can potentially be used in cohort studies and trials to measure the intensity of mental healthcare as a predictor of outcome. Further study into the psychometric characteristics of the Mental Healthcare Intensity Scale is needed.

BackgroundMeasurement-Based Care (MBC) is the routine administration of measures, clinicians' review of the feedback and discussion of the feedback with their clients, and collaborative evaluation of the treatment plan. Although MBC is a promising way to improve outcomes in clinical practice, the implementation of MBC faces many barriers, and its uptake by clinicians is low. The purpose of this study was to investigate whether implementation strategies that were developed with clinicians and aimed at clinicians had an effect on (a) clinicians' uptake of MBC and (b) clients' outcomes of MBC. MethodsWe used an effectiveness-implementation hybrid design based on Grol and Wensing's implementation framework to assess the impact of clinician-focused implementation strategies on both clinicians' uptake of MBC and outcomes obtained with MBC for clients in general mental health care. We hereby focused on the first and second parts of MBC, i.e., the administration of measures and use of feedback. Primary outcome measures were questionnaire completion rate and discussion of the feedback with clients. Secondary outcomes were treatment outcome, treatment length, and satisfaction with treatment. ResultsThere was a significant effect of the MBC implementation strategies on questionnaire completion rate (one part of clinicians' uptake), but no significant effect on the amount of discussion of the feedback (the other part of clinicians' uptake). Neither was there a significant effect on clients' outcomes (treatment outcome, treatment length, and satisfaction with treatment). Due to various study limitations, the results should be viewed as exploratory. ConclusionsEstablishing and sustaining MBC in real-world general mental health care is complex. This study helps to disentangle the effects of MBC implementation strategies on differential clinician uptake, but the effects of MBC implementation strategies on client outcomes need further examination.

Anxiety disorders, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are among the most prevalent mental disorders across the lifespan. Yet, it has been suggested that there are phenomenological differences and differences in treatment outcomes between younger and older adults. There is, however, no consensus about the age that differentiates younger adults from older adults. As such, studies use different cut-off ages that are not well founded theoretically nor empirically. Network tree analysis was used to identify at what age adults differed in their symptom network of psychological functioning in a sample of Dutch patients diag-nosed with anxiety disorders, OCD, or PTSD (N = 27,386). The networktree algorithm found a first optimal split at age 30 and a second split at age 50. Results suggest that differences in symptom networks emerge around 30 and 50 years of age, but that the core symptoms related to anxiety remain stable across age. If our results will be replicated in future studies, our study may suggest using the age split of 30 or 50 years in studies that aim to investigate differences across the lifespan. In addition, our study may suggest that age-related central symptoms are an important focus during treatment monitoring.

Therapists, including group therapists, can systematically gather feedback from patients about how their group members are responding to treatment. However, results of research on using feedback-informed group treatment (FIGT) are mixed, and the underlying mechanisms responsible for positive patient changes remain unclear. Therefore, the present qualitative study examined the perceptions and experiences of both (a) group therapists and (b) group members regarding using feedback in their therapy groups to gauge treatment progress, across five different therapy groups. Specifically, three interpersonal psychotherapy groups and two cognitive-behavioral therapy groups used a FIGT tool in which treatment progress updates were provided to patients and therapists. Observational data were collected in the form of feedback discussions in these therapy groups, as well as during interviews conducted with patients and therapists. Data were analyzed using thematic analysis and a grounded theory approach. Overall, patients were mostly positive about their experiences with FIGT, but therapists also expressed concerns about FIGT. Results indicated that FIGT is useful for gaining insight and strengthening the working alliance. In addition, specific group processes were also found to be important, especially interpersonal learning, cohesion, and social comparison. Practical implications are discussed.

People with a severe mental illness often have less social support than other people, yet these people need social support to face the challenges in their lives. Increasing social support could benefit the person’s recovery, but it is not clear whether interventions that aim to improve social support in people with a severe mental illness are effective. A systematic literature search and review in MEDLINE (PubMed), PsycINFO, CINAHL, Cochrane, JSTOR, IBSS, and Embase was performed. Studies were included if they had a control group and they were aimed at improving social support in people with a severe mental illness who were receiving outpatient treatment. Summary data were extracted from the research papers and compared in a meta-analysis by converting outcomes to effect sizes (Hedges’s g). Eight studies (total n = 1538) that evaluated ten different interventions met the inclusion criteria. All but one of these studies was of sufficient quality to be included in the review. The studies that were included in the meta-analysis had a combined effect size of 0.17 (confidence interval: 0.02 to 0.32), indicating a small or no effect for the interventions that were evaluated. A subgroup analysis of more personalized studies showed a combined effect size of 0.35 (CI = 0.27 to 0.44), indicating a noteworthy effect for these more personalized studies. This evaluation of interventions aimed at improving social support in people with a severe mental illness suggests that these interventions in general have little or no clinical benefit. However, in a subgroup analysis the more personalized interventions have a larger effect on improving social support and merit further research.

Monitoring treatment progress by the use of standardized measures in individual therapy, also called feedback-informed treatment (FIT), has a small but significant effect on improving outcomes. Results of FIT in group therapy settings are mixed, possibly due to contextual factors. The goals of this study were to investigate the feasibility, acceptability and effectiveness of a feedback-informed group treatment (FIGT) tool, based on the principles of the Contextual Feedback Theory and earlier FIGT research. Patients with anxiety or depressive disorders following interpersonal or cognitive behavioural group psychotherapy (IPT-G or CBT-G) were randomized to either feedback (n=104) or Treatment As Usual (TAU; n=93). In the feedback condition, patients filled out the Outcome-Questionnaire 45 (OQ-45) weekly in a FIGT tool and therapists were instructed to discuss the results in each session. Dropout, attendance and outcomes were measured. Additionally, in the feedback condition, OQ-45 response, feedback discussions and acceptability by patients and therapists were assessed. Results showed no differences on dropout, but lower attendance rates in the feedback condition. Although therapists reported high rates of feedback use and helpfulness, patients experienced that results were discussed with them only half of the time and they were also less opti-mistic about its usefulness. The findings indicate that the FIGT instrument was partially feasible, more acceptable to therapists than patients, and was not effective as intended. Future research is needed to discover how feedback can be beneficial for both therapists and patients in group therapy.

BACKGROUND:Most psychotherapy outcome research focuses on symptom reduction as a primary outcome. However, most patients do not seek psychological treatment exclusively for symptom relief, but mainly because they can no longer do what they want to do or used to do. Therefore, besides symptom reduction, also disability in daily functioning should be a focus of psychotherapy outcome research. Yet, until now there is a paucity in research pertaining to the relation between symptom reduction and reduction of disability during psychological treatment.AIMS:For this reason, the aim of the current study was to examine the relationship between changes in symptom reduction (reduction in general symptom distress) and changes in self-reported disability over a period of two years in patients that receive psychotherapy for mood and anxiety disorders (N = 1182).RESULTS:We found strong correlations between both outcome measures at all measurement points. Furthermore, results demonstrated a decrease in both outcome measures from start to end of treatment with a moderate effect for symptom distress and a small effect for experienced disability. Cross-lagged panel analysis demonstrated that a decrease in symptom distress predicted a subsequent decrease in self-reported disability, and a decrease in self-reported disability equally predicted a subsequent decrease in experienced symptom distress.CONCLUSION:Our results seem to indicate that both outcome measures are interchangeable in psychotherapy outcome studies for internalizing disorders.

In recent years, there has been an increasing focus on routine outcome monitoring (ROM) to provide feedback on patient progress during mental health treatment, with some systems also predicting the expected treatment outcome. The aim of this study was to elicit patients' and psychologists' preferences regarding how ROM system-generated feedback reports should display predicted treatment outcomes. In a discrete-choice experiment, participants were asked 12-13 times to choose between two ways of displaying an expected treatment outcome. The choices varied in four different attributes: representation, outcome, predictors, and advice. A conditional logistic regression was used to estimate participants' preferences. A total of 104 participants (68 patients and 36 psychologists) completed the questionnaire. Participants preferred feedback reports on expected treatment outcome that included: (a) both text and images, (b) a continuous outcome or an outcome that is expressed in terms of a probability, (c) specific predictors, and (d) specific advice. For both patients and psychologists, specific predictors appeared to be most important, specific advice was second most important, a continuous outcome or a probability was third most important, and feedback that includes both text and images was fourth in importance. The ranking in importance of both the attributes and the attribute levels was identical for patients and psychologists. This suggests that, as long as the report is understandable to the patient, psychologists and patients can use the same ROM feedback report, eliminating the need for ROM administrators to develop different versions.

A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.