Bond creating with the adolescent mother: glimpsing child care

Objective: this article aims to describe the relationship and bond formation among the nurse and the teenage mother in the process of childcare consultation. Methods: qualitative study. Five nurses and three teenagers took part as subjects to the study. Such study was conducted from February to May 2011 in a Basic Health Unit in Fortaleza-CE, using systematic observation and semi-structured interview. The information gathered from the interviews was submitted to the Bardin content analysis method. The study was approved by the Ethics Committee of the State University of Ceará, on opinion number 04252522-5. Results: category 1: nursing consultation in childcare promotes bonding between the nurse, the mother, the child and the family. Category 2: the nurse reinforces the co-responsibility with the teenage mother. Conclusion: receiving with respect and dignity was mentioned as an important strategy to welcome the teenager and to bond with her

Frequency of pathological gambling among substance abusers under treatment

OBJECTIVE: To investigate the frequency of pathological gambling among alcohol and other substance abusers who sought specialized treatment. METHODS: Seventy-four outpatients from three different substance abuse treatment services were interviewed. The South Oaks Gambling Screen (SOGS) scale was used for the diagnosis of pathological gambling. The diagnosis of alcohol and other substances abuse was established according to the DSM-IV criteria and the Short Alcohol Dependence Data (SADD) scale. The Portuguese version of the Self-Report Questionnaire (SRQ) scale was used to detect psychiatric symptoms and the Center for Epidemiological Studies Depression Scale (CES-D) for depressive symptoms. Average scores obtained from the application of these scales were compared using the Student t-test. RESULTS: All subjects met the criteria for drug abuse, 61.6% met the alcohol dependence criteria, 60.3% for cocaine/crack, and 34.2% for cannabis. According to the SOGS scale, the majority of drug addicts (70.3%) were classified as social gamblers, 10.8% as problem gamblers and 18.9% as pathological gamblers. Psychiatric and depression symptoms were found in the sample. Pathological gambling patients showed more depression symptoms than non-pathological gambling patients. CONCLUSIONS: A high frequency of pathological gambling was found among the drug addicts interviewed. It is emphasized the importance of investigating pathological gambling among patients under treatment of drug abuse and to include strategies for the treatment of this disorder.OBJETIVO: Investigar a freqüência de jogo patológico entre dependentes de álcool e/ou outras drogas que procuraram tratamento em serviço especializado. MÉTODOS: Foram entrevistados 74 pacientes de três serviços especializados em tratamento de farmacodependência. Para diagnóstico de jogo patológico foi utilizada a escala SOGS (South Oaks Gambling Screen). O diagnóstico de dependência de álcool e de outras drogas foi estabelecido a partir dos critérios do DSM-IV e da escala SADD (Short Alcohol Dependence Data). Foram aplicadas as versões brasileiras das escalas SRQ (Self Report Questionnaire) para detecção de sintomas de psiquiátricos e CES-D (Center for Epidemiological Studies Depression Scale) para sintomas depressivos. As médias da pontuação obtida nessas escalas foram comparadas pelo teste t de Student. RESULTADOS: Todos os sujeitos preencheram critério para farmacodependência, sendo que 61,6% preencheram critérios para dependência de álcool 60,3% para cocaína/crack, e 34,2% para maconha. Segundo a escala SOGS, a maioria dos farmacodependentes (70,3%) foi classificada como jogador social, 10,8% como "jogador problema" e 18,9% como jogador patológico. Confirmou-se a presença de sintomas psiquiátricos e depressão na amostra. Pacientes jogadores patológicos apresentaram mais sintomas depressivos que pacientes não jogadores patológicos. CONCLUSÕES: Foi encontrada alta freqüência de jogo patológico entre os farmacodependentes entrevistados. Os resultados mostram a importância da investigação de jogo patológico em pacientes farmacodependentes e inclusão de estratégias para o tratamento desse transtorno nos programas de tratamento

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost