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Existing research finds that leaders develop international reputations based on their past behavior on the international stage. We argue that leaders' domestic choices can also influence their international reputations, perhaps as much as their past foreign policy decisions do. Using formal theory and intuitive argumentation, we develop an overarching framework to predict how much any domestic choice will affect a leader's international reputation. We theorize that certain domestic choices can inform expectations about future international crisis behavior based on the extent to which (1) the costs at stake are similar to those of an international crisis and (2) the domestic issue is salient relative to foreign policy. We use conjoint experiments and other evidence to show that many domestic choices have significant international reputational effects. There is some evidence that the reputational effect of certain domestic choices may equal that of fighting in a previous international crisis.

Abstract

Objectives
To examine work characteristics in relation to body mass index (BMI) and risk of obesity.


Methods
We analyzed data from 1150 participants working 20+ h week−1 from the 2014 National NIOSH Quality of Work Life Survey, based on a representative sample of US workers. We used multiple linear regression for BMI and multiple logistic regression for obesity to estimate associations with 19 different work characteristics plus one set of occupational categories controlling for age, gender, race/ethnicity, education, marital status, job physical exertion, and television watching.


Results
We found significant positive linear associations between BMI and night shift (versus day shift) schedule (B = 2.28, P = 0.008) and blue-collar (versus management/professional) work (B = 1.75, P = 0.008). Night shift schedule [odds ratio (OR) = 2.19, P = 0.029], sales/office work (OR = 1.55, P = 0.040), and blue-collar work (OR = 2.63, P = 0.006) were associated with increased risk of obesity versus 'healthy weight'. No other statistically significant associations between work characteristics and BMI or obesity were observed.


Conclusions
Night shift schedule and blue-collar work were related to increased BMI and obesity risk in US workers in 2014. Identifying risk factors in blue-collar work and redesigning jobs to reduce those risk factors, and reducing night shift work, could play a role in reducing the prevalence of obesity in the USA.

Purpose: This study contributes to the emerging literature on lesbian, gay, bisexual, transgendered, and queer (LGBTQ) health disparities and tobacco use by examining the motivations for smoking among the New York City (NYC) LGBTQ population. Approach: We used grounded theory and blended methods from 3 grounded theorists—Strauss, Corbin, and Charmaz—for data collection, coding, and analysis. Setting: NYC has extensive legislation to prevent smoking; however, the current smoking prevalence of homosexuals is double that of heterosexuals. Participants: Study participants were leaders from 23 NYC LGBTQ organizations. Leaders were chosen to establish a relationship with community and to ensure cultural sensitivity. Eligibility criteria required holding a leadership position in an organization serving the NYC LGBTQ community. Methods: Interviews were transcribed verbatim and uploaded into Dedoose for analysis. An initial code list was developed from the interview guide. Key themes were identified as the themes with the most number of quotes. Results: Three key themes emerged from our interviews: image, socializing, and stress. Smoking was reported to be a socialization aid and a maladaptive coping technique for stress arising from interactions of conflicting identities. Conclusion: Future smoking cessation interventions among the LGBTQ community should equip smokers with healthy coping mechanisms that address the stressors that arise from the intersections of smokers' many identities.

Career awareness is important in ensuring that individuals choose right career and are motivated in their studies. This study assessed career awareness among university students in Tanzania. Specifically, the study assessed the nature of students' awareness about career choices; the effect of lacking career awareness and strategies of overcoming the challenge of lack of career awareness. To address these objectives, the study utilized 88 university students and 5 lecturers selected from three universities in Dar es Salaam region. Clustered and purposive sampling approaches were used in selecting respondents. They responded to a questionnaire and data were analyzed descriptively using SPSS. The findings revealed that majority of the respondents acquired career awareness at secondary level of education. Role models and professionals were the main source of career awareness to students. Poor performance and suppression of inborn talents were among the effects of lacking career awareness. To overcome the challenge, higher learning institutions should initiate career awareness programmes, the government should review curricula and parents should provide career guidance to their children. It is concluded that, a collaborative role from different stakeholders in ensuring young generations are well guided on their future careers could yield better results.

Central terms and concepts / Pamela Maseko, H. Ekkehard Wolff and Russell H. Kaschula -- Introduction / Russell H. Kaschula, H. Ekkehard Wolff and Pamela Maseko -- Prologue : the essentialist paradox in intellectual discourse on African languages / H. Ekkehard Wolff -- part one. African language empowerment : concept formation and intellectualisation -- 1. Researching the intellectualisation of African languages, multilingualism and education / Russell H. Kaschula and Pamela Maseko -- 2. Language empowerment and intellectualisation through multilingual higher education in South Africa / Pamela Maseko and H. Ekkehard Wolff -- part two. Language planning, terminology development and dictionaries -- 3. Language planning in South Africa : a history / Gregory Kamwendo and Nobuhle Ndimande-Hlongwa -- 4. Language and terminology development in isiXhosa : a history / Koliswa Moropa and Feziwe Shoba -- 5. Multilingual terminology and cognition in assessment / Bassey E. Antia -- 6. IsiXhosa dictionaries, language learning and intercultural communication / Dion Nkomo -- part three. Language in education -- 7. Managing multilingualism in education : policies and practices / Anneke Potgieter and Christine Anthonissen -- 8. University multilingualism : modelling rationales for language policies / Bassey E. Antia -- 9. Language development and multilingualism at the Cape Peninsula University of Technology / Monwabisi K. Ralarala, Eunice Ivala, Ken Barris, Noleen Leach, Linda Manashe and Zakhile Somlata -- 10. Teacher development : promoting a biliteracy approach to epistemic access / Michael Joseph, Nompumelelo Frans and Esther Ramani -- part four. Language in the professions : law, media, science and language technology -- 11. Language and law : 'cultural translation' of narratives into sworn statements / Monwabisi K. Ralarala -- 12. Language and media : isiXhosa in journalism and media studies at a South African university / Jeanne du Toit and Pamela Maseko -- 13. Using African languages to teach science in higher education / Wanga Gambushe, Dion Nkomo and Pamela Maseko -- 14. The development of language technologies in the South African context / Justus C. Roux -- part five. Language, culture and intercultural communication -- 15. Language policy in South Africa through the Sapir-Whorf 'looking glasses' / Russell H. Kaschula and Andre M. Mostert -- 16. Language, intercultural communication and literature / Nogwaja S. Zulu -- 17. Music, literature and multilingualism in the East Cape Opera Company / Hleze Kunju and Russell H. Kaschula -- 18. Beyond language : German Studies in a South African university context / Undine S. Weber, Rebecca S.C. Domingo and Regine B. Fourie

Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Université de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council ofAustralia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 "Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universit? de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy ? EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Funda??o para a Ci?ncia e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 ?Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Publisher Copyright: © 2021 ; White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols foreach fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. ; Peer reviewed