dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and Limitations The present study has several strengths. The survey was conducted in person, allowing for visual confirmation that study participants were drawn from the desired subject pool. Additionally, we were able to quickly scan paper documents to identify and address sources of potential missing data. The sample size of the study was large and included a diverse set of healthcare students. Lastly, this study examines a potential process, namely, depressive and anxiety symptoms, through which poor sleep quality may be exerting its effects on self-rated health. This is an important step in moving the literature forward. There are also several disadvantages of the present investigation. First, given the study design, it is not possible to draw causal conclusions. Second, all data were obtained through subjective assessments, as opposed to objective measures, which has the potential to result in a self-reporting bias.
, 2021). As such, out of the 26 competitive weeks, 20 were included. This led to the average inclusion of 6. 15 ± 0. 97 players per week. Firstly, training loads (total distance, distances covered at specific speeds, and the number of accelerations and decelerations) were initially calculated as absolute values using GPS-derived data. Mean values were calculated daily from the considered weeks (see below). Secondly, relative training loads were calculated according to the demands identified in competitive matches. The mean values for all players' competitive matches of the considered weeks were calculated for the same GPS-derived data (total distance, distances covered at specific speeds, and the frequency of accelerations and decelerations). On average, 4. 9 ± 2. 7 complete matches per player were used to calculate the mean values (CD: 6.
They were asked to keep their arms on their hips from the standing position until landing. Performance was measured using a photocell jump system (Optojump, Microgate, Bolzano, Italy) sampling at 1000 Hz, with jump height (cm) subsequently calculated by proprietary software (Optojump, Version 1. 3. 20. 0, Microgate, Bolzano, Italy). The best performance was retained for analysis. Maximal aerobic speed (MAS) was obtained using a 45 s/15 s incremental intermittent test on a track (45/15 MAS). The starting velocity was 8. 0 km⸱h -1 and increased every 1 min with 0. 5 km⸱h -1 increments. The last speed validated by the players was recorded as their MAS. All tests were performed by the same investigators, scheduled at the same time of day, performed in the same order, and using the same devices at each period (T1, T2, and T3).
Analyses General linear regression models stratified for cases and controls were conducted to explore whether the association between overall childhood maltreatment (and separately childhood abuse and childhood neglect) and IQ (model 1), was attenuated by the inclusion in the model of FH or SZ-PRS (model 2). The analyses were then additionally controlled for sex, age, ethnicity, education, study country, and lifetime cannabis use (model 3); and then also for current use of antipsychotics (model 4) only in the FEP group. Measurement of covariates is described in the Supplementary Materials. Results are reported as regression coefficients (B) and standardized regression coefficients (β). Analyses were run using the Statistical Package for the Social Sciences (SPSS) program version 27. 0.
This is different from our previous analysis of this dataset  [2]  and might be due either to insufficient power, or to the exclusion of non-European ancestry FEP patients (i. e. , potentially the relationship between childhood maltreatment and IQ might be more evident among non-European ancestry FEP patients), or to both. Among both cases and controls, SZ-PRS was weakly associated with IQ and did not attenuate the crude association between childhood maltreatment and IQ. This suggests that childhood maltreatment and genetic liability for schizophrenia may be independently associated with poor cognitive functioning. In conclusion, our findings suggest that two measures of genetic liability for psychosis (FH and SZ-PRS) cannot account for the lower levels of cognition found among adults maltreated in childhood.
Data collection Clinical, biological and therapeutic data were collected from patient records using an electronic case report from (eCRF). Socio-behavioral data and patient-reported outcomes, including HRQL, were collected using self-administered questionnaires (electronic or paper version). The questionnaires also included a speci c module developed to assess knowledge, beliefs, and behaviors in the context of COVID-19. The corresponding items were tested on ve patients during a group debrie ng prior to implementation.
Backward selection was used to identify potential associated factors, andvariables with a p-value less than or equal to the signi cance threshold of 5% were retained in the nal model for each score. The assumptions of linearity, normality, homogeneity, and independence of the residuals were checked before the synthesis of the results. Finally, to maintain good power in our analyses, missing data were categorized as "not reported" and coe cients were not presented in the table. The R Studio/4. 1. 3 for software was used for all the analyses  (23).
Common approach We first initialize all three models using as many common aspects of our models as possible to create initial states based on the observed geometry of the Antarctic Ice Sheet. These common inputs and approaches are summarized in Table  1. Namely, we use observations of bedrock topography, ice surface elevation, and ice thickness from BedMachine Antarctica (Version 2;  Morlighem et al. , 2020). To replicate the current ice flow, we take surface velocities from a recent snapshot in time  (2015) (2016)  from the MEaSUREs Annual Ice Velocity Maps dataset (Version 1;  Mouginot et al. , 2019) , which has a resolution of 1 km and good coverage across the entire ice sheet. Across the surface of the ice sheet, all models initially apply a constant-in-time surface mass balance (later modified for Elmer/Ice and Úa), which is the output from the regional atmospheric climate model RACMO2.
Note that, in general, it would be possible to perturb the system using a number of different control parameters in our models. Given the important role that ice shelves play on inland ice sheet dynamics and grounding-line position, via buttressing forces, we here choose to perturb the system by applying a shift in sub-shelf melt rates. We increase the input ocean temperature, which is assumed to be representative of the conditions at depth on the   (Zwally et al. , 2012). The inset shows a zoom into the Amundsen Sea Embayment sector of West Antarctica. Table  2. Comparison of the initial state indicators for the initial states created using Elmer/Ice, Úa, and PISM. All variables, except for flux across the grounding line, were calculated across the same 2 km resolution grid. Reference values for ice extents (total, floating, and grounded) were calculated using the BedMachine dataset  (Morlighem et al.
, 2020) , whereas ice mass values were taken from Table  S3  in the dataset paper  (Morlighem et al. , 2020)  (m SLE, metres sea level equivalent). The total flux across the grounding line was calculated in each respective model. Observed grounding-line flux was taken from  Gardner et al. (2018) , and total surface mass balance is from RACMO2. 3p2  (van Wessem et al. , 2018  continental shelf, by T ∈ {1, 3, 5} • C all around the Antarctic Ice Sheet. This perturbation in temperature is applied for 20 years to create a numerically significant grounding-line retreat. By applying different increases in ocean temperature, we are able to test the robustness of this perturbation and found that a 5 • C perturbation over 20 years remained small; i. e. it resulted in a small but obvious deviation in the position of the grounding line.
The two other terms impose a smoothness constraint for the two inferred parameters β and η. The regularization weights used in this study are λ 1 = 3. 162 × 10 -5 , λ 2 = 1. 259 × 10 2 , and λ 3 = 7. 943 × 10 4. Following the principle of the L-curve analysis, they have been empirically chosen from a large set of inversions, as those that give a good compromise with a low misfit and small regularization terms. As the velocity dataset used for the common initial state is spatially incomplete, the inversion is first performed with a mosaic that aggregates observations from 2007 to 2018  (Mouginot et al. , 2019)  and thus has a nearly complete spatial coverage, but this comes at the expense of the accuracy in areas where velocities have largely changed. The minimization is then continued for 100 iterations using the 2015-2016 dataset to get closer to those observations while staying close to the initially inverted values in areas where observations are missing.
Acknowledgements. This work is part of the  TiPACCs  project, which receives funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 820575. The Elmer/Ice computations presented in this paper were performed using HPC resources of CINES under the allocation 2021-A0060106066 made by GENCI. We further acknowledge the  European Regional Development Fund (ERDF) , the  German Federal Ministry of Education and Research (BMBF) , and  Land Brandenburg  for supporting this project by providing resources on the high-performance computer system at the  Potsdam Institute for Climate Impact Research. The development of PISM is supported by  NSF  grant nos. PLR-1644277  and  PLR-1914668  and  NASA  grant nos. NNX17AG65G  and  20-CRYO2020-0052. We acknowledge the use of the  Northumbria University HPC facility Oswald  to perform the Úa simulations.
We like to thank  Christian Schoof  for providing helpful input on stability analysis. We thank the editor,  Johannes Fürst , for handling our article and one anonymous reviewer and  Alexander Robinson  for their constructive comments that greatly improved the article. Financial support. This research has been supported by  Horizon 2020 (TiPACCs  (grant no. 820575 )). Review statement. This paper was edited by  Johannes J. Fürst  and reviewed by  Alexander Robinson  and one anonymous referee.
Code availability. Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem, last access: 1 August 2023;  Gagliardini et al. , 2013)  and an archived version of the Elmer code is available through Zenodo at https://doi. org/10. 5281/zenodo. 7892180  (Ruokolainen et al. , 2023). All the simulations were performed with version 9. 0 (Rev: 242e4bb) of Elmer/Ice. PISM code is publicly available at E. A. Hill, B. Urruty, R. Reese et al. : Stability of Antarctic grounding lines -Part 1 https://github. com/pism/pism (last access: 1 August 2023) and an archived version of the PISM code is available through Zenodo at https://doi. org/10. 5281/zenodo. 8101891 (Reese et al. ,  2023b). The Úa code is publicly available through GitHub (https://github. com/GHilmarG/UaSource/, last access: 25 July 2023), and an archived version of the model can be found at https://doi.
org/10. 5281/zenodo. 3706623  (Gudmundsson, 2020). Data availability. Model output data are publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 8086403  (Hill et al. , 2023). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-17-3739-2023-supplement.
Participants and procedure We used data from a study on conspiracy theories, registered here: https://osf. io/z9vup/. The study was conducted in April 2020, at the beginning of the COVID-19 pandemic in Poland. The only inclusion criterion was being at least 18 years old, constituting legal adulthood in Poland. The sample consisted of 97 participants; however, 23 were non-native Polish speakers. Thus our final sample consisted of 74 students, including 44 men (60%), 29 women (39%), and one person who described themselves as non-binary. The participants were students recruited from the participants of a social communication course at the University of Economics and Human Sciences. Participants who finished the study obtained extra credits for the course. The mean age of the participants was 25.
Statistical analysis In order to test H1 and H2, we used r-Pearson correlation and multiple linear regression. The analyses were performed with SPSS 21. None of the variables reached an absolute value of skewness higher than two nor an absolute value of kurtosis higher than 7. Thus the assumption of normality of the distribution was not violated  [72, 73]. Pretraumatic stress was positively correlated with agreeableness and negatively correlated with emotional stability and intellect/imagination. Unexpectedly, pretraumatic stress turned out to be positively correlated with prosocial behavior.
Statistical analysis First, we checked the correlations between the variables with the r-Pearson correlation coefficient. In order to test H1, we used quadratic regression. In order to test H2, we used repeatedmeasures ANOVA. In order to test H3 and H4, we used the random intercept cross-lagged panel model (RI-CLPM). The RI-CLPM was performed with the powRICLPM  [93]  and lavaan packages in R 4. 1. 0  [94]. Other analyses were performed with SPSS 21.
Pretraumatic stress in Wave 2 was predicted by levels of prosocial behavior in Wave 1. In order to make sure that our analysis had adequate power, we performed a post-hoc power analysis with the powRICLPM package in R  [93]. We used standardized variables. It turned out that the power for the effect of prosocial behavior in Wave 1 on pretraumatic stress in Wave 2 was 1-β =. 98, which is a good score. Nonetheless, pretraumatic stress in Wave 3 was not predicted by levels of prosocial behavior in Wave 2. 3. 4. 7. Received social support and pretraumatic stress-longitudinal analysis. Considering the low but positive correlation between received social support and pretraumatic stress in Wave 1, we tested the link between received social support and pretraumatic stress across the waves. We conducted a random intercept cross-lagged panel model analysis to check whether social support received in Wave 1 predicted pretraumatic stress in Wave 2 and social support received in Wave 2 predicted pretraumatic stress in Wave 3.
This cross-sectional observational study compares the health behaviors of university students in France and Italy, examining how their choices and lifestyles were affected by the COVID-19 pandemic with the aim of contributing to the development of adequate public health and higher education institutions interventions. The French cohort was investigated between January and February 2022, while the Italian cohort was examined between March and April 2022. In both contexts, data were collected through web surveys using institutional directories of university degree programs. Data were collected using standardized tools, validated and recovered in full or partial form. The tool used consisted of three specific sections (general experience, eating habits, physical activity), to which a fourth, dedicated to describing the sociographic picture of the respondents, was added.
Methodology Based on investigations carried out in France, a suitable number of behaviors likely to be conditioned by the pandemic were selected from the literature. Data was collected for these behaviors using standardized tools, validated and recovered in full or partial form. The tool used in the Italian context which is part of a larger French data set, consisted of three specific sections, to which a fourth, dedicated to describing the sociographic picture of the respondents, was added. The first section examined the general experience of the students, also in comparative terms, before and during the pandemic, seeking to provide an initial picture of students' habitual daily behaviors. The second section focused on eating habits without neglecting possible deviations related to eating disorders (SCOFF questionnaire -Sick Control One Stone Fat Food)  [29]  and the possible use or abuse of alcohol and cigarettes.
The third framework, based on a scale known in the literature as the IFIS questionnaire -International Fitness Scale  [30]  was used to assess the level of physical activity that characterized the subjects' daily lives. In both contexts, data were collected through web surveys using institutional directories of university degree programs. Students were invited to complete the survey by a message explaining the purposes of the initiative. This message was followed up, about one week later, by a reminder message, whose aim was to boost the participation rate. The survey was organized by the authors of the research. The focus groups of students involved consisted of students enrolled in the same year of academic courses, the compilation of the questionnaire was solicited by peer tutors trained by the researchers.
66 (SD = 0. 19), outperform its French counterpart, which showed a value of 0. 59 (SD = 0. 19). As shown by the graph (Fig  1 ), which also breaks down the data by gender, the relative distributions show the presence of some downward outliers presumably attributable to subjects experiencing problems. The fact that the Italian cohort was found to be in better shape than its French counterpart during the pandemic is likely due to the presence of students from the Physical Education for Prevention and Health degree program in the Italian cohort. Focusing on gender, the apparent superior performance of males is statistically significant in both contexts, though it is more marked in the French cohort. Among the many items used in the survey, some of them, in aggregate or single form, are indicative of more complex aspects or behaviors.
Search strategy We conducted our review following a published protocol with PROSPERO registration ID CRD42020221141  [35]. The initial study protocol was written before the completion of the scope searches. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines 2020  [36]. We conducted our literature search in November 2020, with the final search update in August 2023, using PubMed (MEDLINE, PubMed Central), EMBASE, Psy-cINFO via OVID and the Cochrane Library (CENTRAL, Cochrane clinical answers, Cochrane database of systematic review) databases as well as clinical trials registries (clinical trials. gov, EU clinical trials, WHO International Clinical Trials Registry Platform) in order to ensure the screening of unpublished or ongoing trials.
Study selection, inclusion and exclusion criteria We included all randomized controlled parallel group trials using multisession MBIs for SUD or behavioral addictions (BA) in residential or outpatient settings, counting craving as a primary or a secondary outcome, using all types of craving scales or questionnaires and with data available at a post-treatment time point. We retained studies published in English and French. Studies using all types of control conditions were included. Unpublished trials, conference abstracts, and thesis papers were also eligible for inclusion. For comparability, we did not include trials in populations with SUD associated with another condition (posttraumatic stress disorder, depression). Our initial November 2020 protocol was edited in March 2021 to narrow down the inclusion criteria in order to obtain better comparability.
D. , B. T. ) assessed titles and abstracts for inclusion criteria in a blind mode using Rayyan QCRI  [39] , a systematic review automation tool. Any disagreement after unblinding was resolved by discussion, and when consensus was not obtained, the third rater (B. P. ) resolved conflicts. Full texts and references of included articles were then reimplemented on Rayyan QCRI application for blind full-text assessment by two raters (A. D. , B. T. ). Then, after discussion, conflicts were resolved by the third rater (B. P. ). In case of multiple reports on one study, we only used the data set in which craving measures were reported.
Data extraction and synthesis The data extraction form was written by A. D. It included the year of recruitment, the year of publication, study location, study funding, study design, availability of the study protocol, blinding, sample size, population characteristics (gender, diagnosis, consumption status), intervention and control characteristics (type of mindfulness intervention, duration, instructors adherence assessment, presence of homework assignments, type of control intervention), as well as all available outcome data with the type of craving scale or questionnaire, data collection points, and results. When different kinds of control conditions were used, the results from the active control group were extracted  [40]. Studies were ordered by year of publication. The extraction form was first pilot tested by two authors with 3 randomly selected studies and then it was applied to the data from the included publications.
Data analysis In every included study, outcomes were expressed as means with standard deviations or standard errors. Standard errors were converted into standard deviations using the formula SD = SE x sqrtN  [40]. Given the continuous nature of the outcome and the use of different scales and questionnaires for craving assessment in the included studies, we used the standardized mean difference (SMD) to estimate the effect size. The means, the standard deviations in intervention and control groups at post-treatment, and the number of patients in each group were extracted and implemented into Revman 5. 4. 1 software  [41]. In our analysis, we included the available measured data at post-treatment. We considered the potential impact of the missing data on the results in our evaluation. We did not inflate the sample size of the available data up to the total numbers of randomized participants.
Study selection Figure  1  shows the detailed selection of included trials (details of excluded trials are available in Additional file 2). Seventeen articles were included, totaling 1228 patients. Final search round in August 2023 retrieved 11 additional trials, of which 7 were excluded (cf. Additional file 2). Three corresponding authors of published trials were contacted for precisions about post-treatment outcomes: two authors  [49, 50]  responded and the data was used in the analysis, one author  [51]  described a high level of attrition at post-treatment with far less attrition at follow up, so this study was excluded post hoc.
Intervention durations varied from five consecutive days to 12 weeks. Control conditions were sometimes but not always matched on the treatment contact time and theme. Several trials did not have an active control condition using a waitlist, general information sessions, or no intervention. One trial used a sham meditation on a PDA. The outcome assessments were based on self-reported data using scales (Visual Analog Scale or 1-7 Likert scale) and questionnaires. Detailed description of these scales and questionnaires is available in the Additional file 2. The post-treatment time point depended on the intervention duration and varied from 5 days to 12 weeks. The included studies were mostly conducted with just the researcher being blinded or in open label conditions. Retention rates depended on study durations and varied from 100% (for a 5-day intervention) to 34% (in the study with 4 weeks of standard treatment and 8 weeks of intervention).