APPRECIATING DIFFERENCE: ROY SCHAFER ON PSYCHOANALYSIS AND WOMEN

The author describes and appreciates Roy Schafer's critique of Freud's view of female psychology and his other contributions to the psychoanalytic literature on women, noting his then‐novel emphasis that took into account social and cultural factors in analytic treatment. She relates the influence on Schafer's work of his ambiance in that era: the Yale University Student Health Services during the social turmoil of the 1970s (where she was his supervisee), with the university becoming coed, as well as the theoretical plurality even in the early days of the Western New England Psychoanalytic Institute.     

Intimate partner abuse of educated,employed black South African women: sociocultural factors

This study aimed to explore the influence of African culture and tradition on the abuse experiences of Black South African women who are educated and employed. Interviews were conducted with seven Black South African women (age range 38 to 47) who are college educated and employed. All participants were married for seven or more years and self-reporting to be in an abusive relationship. Interviews were transcribed verbatim and analysed using thematic content analysis. The results indicate sociocultural factors such as patriarchal values within their culture as an underlying reason for their husbands’ abusive behaviour. Abuse seemed likely when the women perceived gender roles that were in conflict with their husband's gender role expectations of them. The cultural expectation that women should not leave a marriage appeared to play an implicit role in their staying in an abusive relationship.     

Etude des Facteurs psychosociaux,du burnout,de la Vigueur et des troubles musculosquelettiques chez les soignants hospitaliers

Psychosocial factors, the burnout and the musculoskeletal disorders coexist in health professionals at the hospital. From 176 answers to a questionnary built from Karasek (1990) and Siegrist (1996) theories of psychosocial factors, Shirom (2004) of burnout and vigor and finally Kuorinka (1987) of musculoskeletal disorders, we measure and put in link these different aspects, then we propose a predictive model for burnout and pains.     

Personality Factors Explaining Career Success for Soldiers: An Analytical Review

Personality factors influence career success of soldiers in the military. This article focuses on the diversity and complexity of the nature of the military as a working environment. In this regard the article focuses on general personality characteristics (like locus of control, ego power, etc.) and specific personality characteristics for pilots and divers as an example to explain career success with the person-environment fit model as moderator, in the military.     

困惑与忧思:医学的边界在何处

现代医学将治未病的理想变成了现实。对疾病危险因素的控制, 癌症前期病变的基因检测, ECT等高清晰度的影像技术对人体微细病变的勾画, 以及生命的正常过程、个人和社交问题的医疗化, 疾病诊断标准不停歇的放宽, 使得健康与疾病的界线愈来愈模糊, 医学进步到没有健康人了。区分生命正常过程与疾病的不同, 明确医学的边界, 避免医学对人体过度的破坏性的干预, 杜绝对医疗资源的浪费, 是当今医学迫需努力解决的课题。     

真实性的影响因素及干预的研究进展

从20世纪末开始,西方真实性研究开始突破弗洛伊德理论的限制,得以蓬勃发展。目前研究着重于探讨家庭环境、个性和性别因素对真实性的影响以及各种真实性干预方法的有效性问题,如会心团体、觉知训练和自我肯定性训练在提高个体真实性中的作用。研究存在的问题是真实性的跨文化一致性仍然存在争议,不能把西方研究结果照搬到我国。未来的研究趋势集中于定性和定量研究方法相结合开展真实性的本土化研究,以及开展中国人的真实性干预研究。     

无医学指征剖宫产的影响因素及对策

无医学指征剖宫产是造成高剖宫产率的一个重要原因,也是公共卫生和全世界医疗机构关注的问题。事实上,许多潜在因素包括社会人口学、传统文化、家庭、法理及医源因素在无医学指征剖宫产形成中发挥了重要作用,并加剧了无医学指征剖宫产这一现象出现。更重要的是,通过构建和谐医疗环境、提高无痛分娩率、加强妊娠期健康宣教、规范产科管理、优化分娩环境和增加人文关怀等合理干预可有效降低无医学指征剖宫产。期望为卫生政策制定、临床指南和促进该领域未来的临床实践提供有意义的参考。

     

ICD-11 prolonged grief disorder: Prevalence,predictors, and co-occurrence in a large representative sample

The International Classification of Diseases has recently defined Prolonged Grief Disorder (PGD) with symptoms such as longing, worry, and intense emotional pain that exceed sociocultural norms more than 6 months after the loss. This study aims to (a) estimate the prevalence of this new diagnostic category, (b) identify its sociodemographic and loss-related predictors, and (c) assess the co-occurrence of PGD with other psychological disorders and substance abuse. A large representative sample of Spanish adults (N = 1498) participated. Several multivariate binary logistic regression and multivariate logistic regression models were used. Results showed a 9.95% prevalence in the total sample. Catholic beliefs were a positive predictor, while higher income and more time since loss significantly decreased the odds of PGD. PGD significantly increased the likelihood of anxiety, depression, somatisation, post-traumatic stress disorder, loneliness and substance use. Our study contributes to assessing the multicultural PGD validity, as our results from a large representative sample are comparable to those in other countries with the PGDS. Our findings have direct implications for the assessment and treatment of bereavement, identifying for practitioners variables that make individuals more vulnerable to PGD. Results highlighted the high co-occurrence of PGD with other psychological illnesses and increased drug use.     

Using MCMC chain outputs to efficiently estimate Bayes factors

One of the most important methodological problems in psychological research is assessing the reasonableness of null models, which typically constrain a parameter to a specific value such as zero. Bayes factor has been recently advocated in the statistical and psychological literature as a principled means of measuring the evidence in data for various models, including those where parameters are set to specific values. Yet, it is rarely adopted in substantive research, perhaps because of the difficulties in computation. Fortunately, for this problem, the Savage–Dickey density ratio (Dickey & Lientz, 1970) provides a conceptually simple approach to computing Bayes factor. Here, we review methods for computing the Savage–Dickey density ratio, and highlight an improved method, originally suggested by Gelfand and Smith (1990) and advocated by Chib (1995), that outperforms those currently discussed in the psychological literature. The improved method is based on conditional quantities, which may be integrated by Markov chain Monte Carlo sampling to estimate Bayes factors. These conditional quantities efficiently utilize all the information in the MCMC chains, leading to accurate estimation of Bayes factors. We demonstrate the method by computing Bayes factors in one-sample and one-way designs, and show how it may be implemented in WinBUGS.