Feminist Attitudes of Non-labelers

Recent studies on those who label themselves as feminists and non-feminists have become a popular topic of research. Past research has found that many women hold feminist values, but don’t label themselves as feminists. Those who identify as feminists have been found to have higher levels of well-being than those who do not identify as a feminist (Zucker & Bay-Cheng, 2010). Other benefits have been found such as empowerment, resilience against sexism, and improved body-image (Zucker & Bay-Cheng, 2010). Yet, many women continue to not identify with labeling themselves as feminists (Fitz, Zucker, & Bay-Cheng, 2012). In the current study, we examined the phenomenon of “I’m a feminist, but…” through the relationship between feminists, non-labelers, and non-feminists. Specifically, we analyzed how they differ on feminist identity attitudes via the Feminist Identity Composite Scale consisting of five attitudes: passive acceptance, revelation, embeddedness-emanation, synthesis, and active commitment (FIC; Fischer et al., 2000). We collected usable date from 337 female undergraduate students from two universities. They were asked to indicate agreement with core feminist beliefs (Zucker, 2004), indicate whether they identified as a feminist, and complete a survey regarding feminist identity questions. A multivariate analysis of variance (MANOVA) was ran to test the multiple dependent variables. Preliminary results indicate a significant difference among non-labelers, feminists, and non-feminists. This suggests that researchers and practitioners in psychology should consider non-labelers to be a unique group of women separate from clear feminists and non-feministshttps://openriver.winona.edu/urc2019/1027/thumbnail.jp

Initial severity of depression and efficacy of cognitive-behavioural therapy: individual-participant data meta-analysis of pill-placebo-controlled trials

BACKGROUND: The influence of baseline severity has been examined for antidepressant medications but has not been studied properly for cognitive-behavioural therapy (CBT) in comparison with pill placebo. AIMS: To synthesise evidence regarding the influence of initial severity on efficacy of CBT from all randomised controlled trials (RCTs) in which CBT, in face-to-face individual or group format, was compared with pill-placebo control in adults with major depression. METHOD: A systematic review and an individual-participant data meta-analysis using mixed models that included trial effects as random effects. We used multiple imputation to handle missing data. RESULTS: We identified five RCTs, and we were given access to individual-level data (n = 509) for all five. The analyses revealed that the difference in changes in Hamilton Rating Scale for Depression between CBT and pill placebo was not influenced by baseline severity (interaction P = 0.43). Removing the non-significant interaction term from the model, the difference between CBT and pill placebo was a standardised mean difference of -0.22 (95% CI -0.42 to -0.02, P = 0.03, I2 = 0%). CONCLUSIONS: Patients suffering from major depression can expect as much benefit from CBT across the wide range of baseline severity. This finding can help inform individualised treatment decisions by patients and their clinicians.R01 MH060998 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; K02 MH001697 - NIMH NIH HHS; R01 MH060713 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

En opeens ging het beter!:De rol van <em>sudden gains</em> in cognitieve therapie en interpersoonlijke therapie voor depressie

We onderzochten de frequentie, kenmerken, voorspellers en impact van zogenaamde sudden gains — grote en stabiele symptoomverbeteringen tussen twee opeenvolgende therapiesessies — in cognitieve therapie (CT) en interpersoonlijke therapie (IPT) voor depressie. Ook werd de invloed van het tijdsinterval tussen de sessies bekeken. 117 depressieve patiënten ontvingen zestien tot twintig sessies CT of IPT. De ernst van depressie werd elke sessie gemeten met de BDI-II. Sudden gains werden gedefinieerd volgens de oorspronkelijke criteria van Tang en DeRubeis. Er waren significant meer patiënten met sudden gains in CT (42,2%) dan in IPT (24,5%). We vonden geen groepsverschillen wat betreft grootte, timing en voorspellers van de gains. Patiënten met sudden gains hadden een beter behandelresultaat, zowel op de korte als op de lange termijn. Het tijdsinterval tussen sessies beïnvloedde de conclusies niet. De bevinding dat twee therapieën met een vergelijkbaar behandelresultaat verschillen wat betreft de frequentie van sudden gains wijst mogelijk op verschillende onderliggende werkingsmechanismen. This study examined the rates, characteristics, baseline predictors and clinical impact of sudden gains in cognitive therapy (CT) and interpersonal psychotherapy (IPT) for depression. 117 depressed adult outpatients received sixteen to twenty sessions of either CT or IPT. Session-by-session symptom severity was assessed with the BDI-II. Sudden gains were examined using the original criteria. Furthermore, the influence of the duration of the between-session-interval at which sudden gains were recorded was explored. There were significantly more patients with sudden gains in CT (42.2%) than in IPT (24.5%). No differences were found with regard to magnitude, timing and predictors of the gains. Those patients with sudden gains were less depressed at post-treatment and follow-up. The duration of the between-session-interval did not influence the pattern of results. This study indicates differences in occurrence of sudden gains in two interventions that overall show similar results, which might reflect different mechanisms of change

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomized control trials

Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety co-morbidity, and consider the association of a wide range of symptoms with treatment outcomes. / Method: Individual patient data from six RCTs of depressed patients (total n=2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention timepoints using individual items and sum scores. Symptom networks (Graphical Gaussian Model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators. / Results: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms. / Conclusion: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology

The Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to mindfulness-based cognitive therapy (MBCT). Using previously published data ( N = 424), we constructed prognostic models using elastic-net regression that combined demographic, clinical, and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: area under the curve [AUC] = .68) predicted relapse better than baseline depression severity (AUC = .54; one-tailed DeLong’s test: z = 2.8, p = .003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared with individuals who maintained ADM (48% vs. 70% relapse, respectively; superior survival times, z = −2.7, p = .008). For individuals with moderate to good ADM prognoses, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database [version 2; peer review: 2 approved]

BACKGROUND: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: “disorder severity”. In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of “disorder severity” related factors are needed. AIMS: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) “disorder severity” which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline “disorder severity” and the type of treatment received. METHODS: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline – the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. CONCLUSIONS: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. REGISTRATION: PROSPERO CRD42019129512 (01/04/2019

The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration

Background: Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective: To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method: Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. Results: For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). Conclusions: This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments. © 2014 DeRubeis et al