Variáveis sociodemográficas, psicológicas e tempo na internet como preditoras do uso de redes sociais

Com a crescente expansão das redes sociais em todo o mundo, os investigadores têm procurado compreender os preditores da sua utilização e a relação com a saúde mental e o bem-estar. Este estudo teve como objetivo relacionar diversas variáveis sociodemográficas e psicológicas, bem como o tempo dedicado à internet com o uso de redes sociais representativas em Portugal e a nível global, nomeadamente o Instagram, WhatsApp, Facebook, YouTube, LinkedIn e outras redes (como TikTok, Twitter e BeReal). Mais especificamente, analisar o contributo explicativo de um conjunto de variáveis, entre as quais construtos psicológicos como autoestima, solidão, satisfação com a vida e o comportamento de phubbing na frequência de utilização das redes sociais referidas. Foram analisados os dados de um questionário online aplicado a uma amostra de 306 pessoas adultas, com idades entre os 18 e os 79 anos (M = 38.0, SD = 16.3), incluindo 210 mulheres e 98 homens. Das variáveis consideradas nas análises de regressão categorial, os resultados permitiram identificar o melhor conjunto de preditores do uso de cada rede social. Em termos globais, o comportamento de phubbing e a idade foram as variáveis com maior poder explicativo da frequência de uso das redes sociais. Foram ainda identificadas variáveis com contributo específico para a predição da frequência do uso de cada rede. Em particular, o sexo e a escolaridade contribuíram para a rede social LinkedIn; a profissão para as redes sociais Facebook, Instagram e LinkedIn; o estado civil fez parte do modelo da rede social Instagram; a perceção de solidão contribuiu para as redes sociais WhatsApp e LinkedIn; o tempo de uso da internet nos dias de semana constou do modelo para a rede social LinkedIn; e, por último, o tempo de uso da internet nos dias de fim de semana fez parte do modelo da rede social YouTube, bem como do modelo obtido para outras redes, tais como Tiktok, Twitter e BeReal. Estes resultados permitem ampliar o conhecimento sobre características sociodemográficas, psicológicas e o tempo de uso da internet em utilizadores adultos de redes sociais em Portugal, promovendo uma reflexão sobre fatores de risco e adaptativos no uso destas plataformas e as implicações para o domínio da saúde mental e o bem-estar.With the increasing expansion of social networks around the world , researchers have sought to understand the predictors of their use and the relationship with mental health and well being. This study aimed to relate several sociodemographic and psychological variables , as we ll as the time spent on internet with the use of representative social networks in Portugal and worldwide, namely Instagram, WhatsApp, Facebook, YouTube, LinkedIn, and other networks ( such as TikTok, Twitter and BeReal ). More specifically, to analyze the explanatory contribution of a set of variables, including psychological constructs such as self esteem, loneliness, life satisfaction, and phubbing behavior in the frequency use of different social networks. Data analyzed came from an online survey applied to a sample of 306 adults, aged between 18 and 79 years ( M = 38.0, SD = 16.3), including 210 women and 98 The results of categorical regression analyses allowed to identify the best set of predictors for the use of each social network. Phubbing beha vior and age were the variables with the greatest explanatory power for the frequency of use of social networks. In addition, variables with a specific contribution in predicting the frequency of use of each network were also identified . In particular, gen der and education level in LinkedIn; employment status in Facebook, Instagram and LinkedIn; marital status in Instagram; loneliness in WhatsApp and LinkedIn; internet time spent on weekdays in LinkedIn, and on weekends in YouTube and in other networks (Tik tok, Twitter and BeReal). These results make it possible to expand knowledge about sociodemographic and psychological characteristics and the time of internet use in adult users of social networks in Portugal, promoting a reflection on risk and adaptive factors in the use of these platforms and the implications for the field of mental health and the well being

Multimodal Anaesthesia in a Crab-eating Fox (Cerdocyon thous) Undergoing Hemilaminectomy and Sacrococcygeal Stabilization

Background:  Several researches have shown the impacts of roads more directly to wildlife in Brazil. The crab-eating fox (Cerdocyon thous) is a frequent run over victim. Dissociative drugs are commonly used, but inhalation anesthesia is indicated in cases of extensive and prolonged surgeries. Despite their similarity with domestic dogs, the literature is scarce regarding the association of new anesthetic techniques and protocols in wild canids. The aim of this paper was to report the viability of multimodal anesthesia in a crab-eating fox, victim of running over, undergoing hemilaminectomy and sacrococcygeal stabilization.Case: An adult male specimen of crab-eating fox was rescued after being run over and taken to a wild animal screening center. Physical examination showed superficial and deep pain, lack of support for the pelvic limbs and proprioception, increased reflexes, and reduced tail mobility. Chemical restraint with intramuscular (IM) tiletamine-zolazepam (6.0 mg/kg) and morphine (0.5 mg/kg) was performed. Meloxicam (0.2 mg/kg IM) and enrofloxacin (5.0 mg/kg IM) were also administered. The animal was sequentially admitted to the veterinary hospital. Radiographic images showed compaction of the spinal column of the T10 and T11 thoracic vertebrae and the sacrococcygeal region. Sixty min after chemical restraint, the anesthesia was supplemented with IM tiletamine-zolazepam (4.5 mg/kg), and fluid therapy with 0.9% NaCl (10 mL/kg/h) was started. Ten min later, intravenous propofol dose-effect (2.5 mg/kg) was administered and general anesthesia was maintained with isoflurane (FiO2 = 1.0). Thirty min after the induction of anesthesia, the animal was urdergoing hemilaminectomy and sacrococcygeal stabilization. Constant rate infusions (CRI) of dexmedetomidine (0.5 μg/kg/h) and ketamine (0.6 mg/kg/h) were started. Lidocaine (7.0 mg/kg) and bupivacaine (2.0 mg/kg) were administered into the surgical site on the T10 and T11 vertebrae at 35 and 80 min into the surgery, respectively. The isoflurane requirement was adjusted often to keep the animal in the surgical anesthetic plan. At the end of the surgery (total time, 95 min), lumbosacral epidural analgesia was performed with morphine (0.1 mg/kg). No important abnormalities were detected in heart rate, systolic arterial pressure, mean arterial pressure, diastolic arterial pressure, respiratory rate, oxygen saturation, or body temperature during the surgical period. The time intervals between the end of anesthesia, and the following events: extubation, the first head movement, and the establishment of sternal were 18, 34 and 73 min, respectively. Recovery was considered calm and peaceful, with no signs of pain or excitement.Discussion: Considering the painful discomfort and the need for manipulation, dissociative anesthesia was initially used to move the animal to hospital care. Due to the immediate indication for surgery, it was decided to use propofol in a sufficient dose for orotracheal intubation, keeping anesthesia with isoflurane. With the expectation of severe pain during the surgical procedure, CRI of dexmedetomidine and ketamine were used, in addition to lidocaine and bupivacaine at the lesion site. Although the minimum alveolar concentration of isoflurane has not been recorded, the physiological parameters were kept relatively stable, ratifying the adequate plan of anesthesia compatible with the observed eye reflexes. Based on the experience with other canids, the use of epidural morphine was performed, aiming at postsurgical analgesic extension. Although a certain lack of coordination was observed, the animal’s recovery was characterized by stillness, with no signs of pain or excitement, confirming the effectiveness of the anesthetic protocol. The present report may aid in the choice of balanced anesthetic approaches in wild canids

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

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