dataesr/scientific-paragraphs-categorization huggingface dataset:
In the trials included in this metaanalysis, craving was measured using different scales and questionnaires, possibly contributing to the high degree of heterogeneity between studies. In 2000, Sayette et al. described a variety of ways to measure craving and emphasized that each of these has limitations  [55]. The systematic use of simple standardized intensity scales combined with psychometrically validated substancespecific questionnaires could contribute to more comfortable meta-analytic data management. In the included RCTs, craving was sometimes evaluated during the actual craving at a precise point in time and sometimes it was described retrospectively, further adding to the differences between studies and complicating interpretation. One potential way to overcome some of these challenges would be to conduct trials using digitally assisted mindfulness meditation.
In addition, we pooled the results from various mindfulness interventions. Although we excluded brief mindfulness training programs for better comparability, there is still a possibility that shorter programs have different impact on clinical outcomes than the longer ones. Our meta-analysis has strengths: we included recent data with more than half of the studies published after 2015. We conducted our study in accordance with current recommendations for systematic reviews and metaanalyses, aiming for a robust methodology. Even though the main result is comparable to other studies, our inconsistency exploration supports the need for the future developments in mindfulness research recommendations. In fact, a 2021 Cochrane review on MBIs in addiction came to similar conclusions, although craving data was not pooled  [6].
One participant was excluded because he could not remember the event we were investigating, making the flashbulb memory questionnaire impossible to administer. To ensure the absence of psychotic disorders in controls, we assessed participants with the 7th version of the Mini-International Neuropsychiatric Interview (M. I. N. I. 7. 0. 0;  Sheehan et al. , 1998). The final sample used for data analysis included 24 patients with schizophrenia and 25 control participants. Sociodemographic and clinical characteristics of the sample are presented in Table  1. The study was carried out according to the code of ethics of the World Medical Association (Declaration of Helsinki). All study procedures were approved by the Institutional Review Board (Local Ethics Committee of the Laboratory Epsylon). All participants provided written informed consent and were not compensated.
Statistical analysis We used IBM SPSS software (version 27) to analyze data. Exploratory analysis showed non-normal distribution for some variables. Missing data were found for one item of the FBM questionnaire in two participants, which were replaced by the median in each group (i. e. , median imputation). No transformation enabled a normal distribution, thus we used non-parametric tests. For non-normal distributed variables, we used Mann-Whitney U exact test  (Field, 2009; Mann & Whitney, 1947)  to analyze group differences. For normally distributed variables, we used the independent two-sample t-test. We reported |r| values to show effect sizes. For normal distributions, we used Pearson's r to explore correlations, while for non-normal distributions, we used Kendall's Tau (τ). We chose Kendall's Tau over Spearman's Rho because of our small sample size  (Field, 2009).
FBM phenomenological characteristics Group differences for the remaining subscales, including usually claimed predictors of FBM (i. e. , Rehearsal, Emotion, Novelty, Consequence, Importance, and Implication) are presented in Table  4. More detailed data (i. e. , frequencies, minimum and maximum) are reported in Supplementary Table  1. As reported in Table  4 , schizophrenia patients rated the emotion subscale significantly higher than control participants (U = 163. 0, p = 0. 006, |r| = 0. 392). In this analysis, we included all items of the emotion subscale, including the emotional valence item.
Search strategy The present systematic literature review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  [76]. A systematic search was conducted on PsycINFO, Web of Science, and Scopus databases using the agreed-upon search string. The following search string was applied in all databases: (patholog* OR problem* OR addict* OR compulsive OR dependen* OR disorder* OR excess* OR impuls*) gambl* OR betting OR gam* AND (Working memory OR Short-term memory). The identified articles were further screened for duplicates and relevance. Referencing software was used to scan for duplicates and to identify all the relevant studies that included the investigation of "memory", and the irrelevant studies were excluded. The remaining studies were then subjected to title, abstract, and full-text screening, respectively.
Bullying occurs when an individual is repeatedly victimised by negative actions performed by peers. As oral features, like malocclusion and dental structural defects, can promote psychological distress, which is also found in those who are bullied, we aimed to study the association between orofacial conditions and bullying. A systematic review (PROSPERO CRD42022331693), including articles dealing with bullying and dentofacial traits, was performed following the PRISMA chart. The iterative search of eligible publications was carried out on 27 March 2023 on four databases (PubMed, PubPsych, Web of Science and Cochrane Reviews) and in the grey literature. Among the 25 articles included, 4 referred to qualitative studies, which analysed 632 interviews with children, 8 interviews with parents, 292 letters, and 321 Twitter posts.
Sources of Information and Search Strategy The iterative search of eligible publications was carried out on 27 March 2023 on four databases (PubMed, PubPsych, Web of Science and Cochrane Reviews) and in the grey literature. The search procedure submitted was: (tooth abnormality OR malocclusion OR amelogenesis imperfecta OR dentinogenesis imperfecta OR dental enamel hypoplasia OR developmental defects of enamel) AND (bullying OR relational aggression OR cyberbullying OR emotional abuse OR harassment OR school violence OR teasing OR victimization) NOT (carie) NOT (decay). The reference list of each study selected has been analysed to identify additional studies that had not been found in the first search.
Study Selection Results obtained from the database research were exported to Rayyan ® software  [18]. Duplicates have been detected and removed. Two evaluators (AB and EN) independently reviewed the titles and abstracts of the publications in order to determine whether or not they met the eligibility criteria mentioned above. When in doubt, the full text was analysed to determine if the article was suitable for inclusion. If the reviewers were not blinded by authorship or the name of the journals, they were blinded to each others' decisions. Disagreements between individual judgements were planned to be resolved by asking a third reviewer (TC) for their opinion.
Study Selection A total of 171 references were obtained after research was conducted on four databases (PubMed, PubPsych, Web of Science, Cochrane Reviews) and grey literature. After the removal of duplicates, 147 articles were screened by a careful reading of their titles and abstracts. A further 120 of them were then excluded because they did not correspond with the aim of this review leaving 27 articles selected for full-text reading and assessment of the eligibility criteria. After full-text reading, two more articles were excluded: one did not focus on the subject of this research, and the other was a case report. In the end, 25 articles were included fulfilling the eligibility criteria (Figure  1 ).
The experimenter verbally informed the participants whether the free throw was successful or not (i. e. binary feedback). No further information was provided regarding the trajectory of the ball. This absence of information obliged them to explore several solutions to find the optimal one  19, 21, 33. The participants of the mixed group carried out the first 60 free throws with the same method as the error-based group and the next 60 free throws with the same method as the reinforcement group. Reinforcement learning was started after error-based learning to keep an ecological dynamic of motor learning  26. In the control group, an active rest without manipulation of the ball replaced the training session. Data recording and analysis. For each session, the number of successful free throws was recorded as follows: where 12 is the number of free throws in each session of pre-test (PreT) and post-test (Post0d, Post1d, and Post7d), as well as during each block of the training session.
One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend, under the premise that there is not a single correct answer to each of these questions. First, there are multiple ways in which even linear trend can be operatively defined. Second, different criteria can be followed when deciding whether the trend observed needs to be taken into account when assessing intervention effectiveness or, in contrast, its magnitude is sufficiently small to be considered inconsequential. If a decision is made to take trend into account, there are different ways in which this can be done. Finally, a challenge arises when there is a trend for some of the comparisons between conditions, but not for others.
, 2015). Two types of trend are particularly relevant clinically. On the one hand, SCED data often has an improving trend in baseline, due to many factors: (1) getting used to being observed by the experimenter, (2) getting familiar with the material used for collecting the target measures, (3) retest effect if the same measure is performed on numerous occasions, TREND ANALYSIS IN SCED DATA (4) developmental effects if SCED are used in children improving for developmental factors and learning unrelated to the intervention being tested, (5) spontaneous recovery when SCEDs are used in a condition that cannot be expected to be totally stable (e. g. subacute acquired brain injury); (6) non-specific clinical management in baseline such as time spend with therapist, definition of treatment goals with the family, motivational factors.
Design, Intervention, and Target Behaviors To illustrate the analytical options, data from  Lebrault et al. (ongoing study registered  in Clinical Trials NCT04560777) will be used, as it used a design particularly useful to rehabilitation: multiple-baseline design (MBD) across participants and across behaviors. In this MBD, all participants begun the intervention with a general baseline of randomized length (3 to 6 baseline measures) during which no intervention was provided and no goal trained. After this general baseline line with no intervention, the moment of introducing the intervention for each goal was staggered and chosen at random among several alternatives, following a Kohler-Levin randomization (see  Levin et al. , 2018 , for a comparison among options for randomization in MBDs).
TREND ANALYSIS IN SCED DATA Fourth, some of the baselines (i. e. , different goals for the same participant) present possible trends, whereas other baselines appear to be stable. This reflects directly the fourth question that will be dealt with in the current text: the need (or lack thereof) of a unitary strategy for dealing with trend across all baselines. Fifth, the data are real (not generated via simulation), recent (ClinicalTrials. gov ID: NCT04560777), and complete (i. e. , not strategically picked) for each of the two participants. Thus, the aim is to illustrate real challenges that applied researchers and clinicians wishing to estimate to provide their clients with personalized evidence-based interventions (ref if published) need to face.
, 1989). A similar approach is followed in the Mean phase difference technique  (Manolov & Rochat, 2015; Manolov & Solanas, 2013) , but with a different method for estimating the trend line, and with the projected baseline trend compared to the actually obtained treatment phase measurements. Specifically, the baseline trend slope is estimated as the average of the differences between successive measurements, whereas the quantification of intervention effect is the average of the differences between each predicted intervention phase value and the corresponding actual data point. Projecting baseline trends requires authors to keep in mind or to avoid two issues: (a) potentially impossible projections (e. g. , negative values when the target behavior is expressed as a frequency, values greater than 100% when the target behavior is expressed as percentage on-task); and (b) potentially unreliable trend projections, based on too few and/or too variable baseline data points, which may lead to unreliable baseline trend estimates and unreasonable projections  (Manolov et al.
Recommendations for Applied Researchers The text so far has emphasized the myriad of options existing for answering each of the four questions. The illustrations provided, despite being very limited and in no way exhaustive, suggest that for some datasets the different data analytical options (for estimating, deciding about, and controlling for trend) would agree making the choice inconsequential. However, for other datasets such agreement may not be present. Thus, applied researchers are facing a difficult task when deciding how to deal with trend. In the context of so many possibilities and lack of a clear consensus, it has been highlighted that the justifications provided for the analytical decisions made are crucial  (Manolov et al. , 2022; Tincani & Travers, in press). Apart from echoing these recommendations, we make further suggestions in the following sections.
Limitations An initial limitation of the current text refers to the data used for commenting the questions that applied researchers may asked themselves in relation to trend. The illustrative data sets used cannot be assumed to represent all possible features of clinical, behavior, neuropsychological, or educational SCED data. It only serves to exemplify certain challenges that can occur when applied researchers are dealing with real data. However, according to the specific data patterns, the different trend line fitting techniques may agree or disagree to a different extent. Furthermore, in certain contexts the decision regarding which trend line fitting technique to use may be trivial or unnecessary, for instance, when an initial stable behavior can be expected (e. g. , a baseline level close to 0, because the individual is unable to perform the target behavior before learning it via the intervention) and an abrupt and maintained change in level can be expected (i.
Future Research A field study could be performed, in the context of real SCED data, in order to quantify the degree to which different analytical approaches to trend agree and the degree to which they end up leading to different conclusions for intervention effectiveness. This could be TREND ANALYSIS IN SCED DATA understood as performing a large scale sensitivity analysis, in relation to the choice of a trend line fitting technique. Another option is to perform a simulation study and check the degree to which using different trend line fitting techniques  (OLS, split-middle, trisplit, Theil-Sen, differencing)  affects several possible quantifications (e. g. ,  Swaminathan et al. 's, 2014 , generalized least squares approach, Center et al. 's, 1985, piecewise regression; Manolov and Rochat's, 2015,  mean phase difference).
METHOD Data source We acquired data from the IMAGEN project, a large-scale, longitudinal, multi-center cohort study of adolescents  [36]. The IMAGEN cohort included a large group of adolescents who were recruited across eight European research centers, including sites in Germany (Berlin, Dresden, Hamburg and Mannheim), the United Kingdom (London and Nottingham), Ireland (Dublin) and France (Paris). Personality, stressful life events, drinking motives and alcohol consumption were assessed at ages 16 (wave 2), 19 (wave 3) and 22 (wave 4) years. The study was approved by all local ethics committees in accordance with the Declaration of Helsinki. Written informed consent was obtained by the legal guardian of the adolescent participant prior to the age of 18, and by the participant thereafter. A more detailed description of the sample composition and study design is provided elsewhere  [36].
05, CFI > 0. 95, TLI > 0. 95)  [46, 47]. We used the psychonetrics package  [48]  for modeling and the qgraph package  [49]  for network visualization. To assess the stability of the final network, we employed a bootstrapping procedure (n = 1000). Strength centrality measures were computed to quantify the relative node importance in the network. For the temporal network, we calculated each node's in-strength (i. e. sum of all ingoing absolute edge weights) and out-strength (i. e. sum of all outgoing absolute edge weights). For the contemporaneous networks, we estimated the node strength, which is defined as the sum of all absolute edge weights that are connected to a given node  [50]. All analyses were carried out using the software R version 4. 1. 2 [51]. This study was not pre-registered, and our results should be considered exploratory.
ACKNOWLEDGEMENTS This study is part of the project ' New Science of Mental Disorders ' (www. nsmd. eu),supported by the  Dutch Research Council  and the  Dutch Ministry of Education, Culture and Science (NWO gravitation  grant number  024. 004. 016 ). This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministerium für Bildung und Forschung  (BMBF grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ;  Forschungsnetz IMAC-Mind   01GL1745B ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ),  NSFC  grant  82150710554  and  European Union  funded project ' environMEN-TAL ', grant no:  101057429.
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitauxde-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ; ( RO1 MH085772-01A1 ) and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to  Knowledge Centres of Excellence.
Further explorations with concurrent verbal tasks could also probe the ability to carry to completion elementary deductions even under load of language resources. The data also point at differences between kinds of logical representations, although our task was less sensitive to them. While both neuroimaging and oculomotor data show that conditions with logical valence were always different from the visually matched conditions without logical content (INF vs. NOV and LOI vs. NON), our task captures differences between the sustained representation of an unknown object (LOI) and the application of a rule of inference (INF) in the whole brain analysis, but only marginally in the oculomotor data. Further studies are needed to address how task demands modulate the signal strength of logical representations across time.
Materials and methods Participants. Twenty-eight adults (20 females; mean age = 23. 41; range age = 18:48) were retained for Experiment 1. Eight additional adults were tested and excluded from the analysis due to poor oculomotor data quality. All of them were recruited through the Center for Brain and Cognition database at Universitat Pompeu Fabra (Spain), where the experiment took place. Another group of twenty adults (12 females; mean age = 24. 57; range age = 18:36) was scanned for Experiment 2. They were recruited through the Cognitive Neuroimaging Unit database at NeuroSpin (France), where the acquisitions were performed. All participants gave written informed consent and received compensation for their participation. These studies were approved by the regional ethical committee Parc Salut del Mar.
Acknowledgements The research was supported by the  Ministerio de Ciencia e Innovación  Grant  PID2019-108494GB-I00  to L. L. B. , by the  McDonnell Foundation Network Grant : ' The Ontogenetic Origins of Abstract Combinatorial Thought ' , and by  FPI   BES-2017-080365 Grant  to A. M. S. We thank anonymous reviewers for their comments and suggestions.
Objective: The World Health Organization recognizes abortion as essential health care and has encouraged governments to ensure access to abortion services throughout the COVID-19 pandemic. However, the threat of infection combined with government responses to COVID-19 have impacted access to abortion services globally. This study explores access to abortion in Germany during the pandemic. Methods: This study used a mixed-methods design. An analysis of data collected by Women on Web (WoW) was carried out to assess women's reasons for choosing telemedicine abortion outside the formal health system in Germany during the pandemic. Descriptive statistics were generated for 2057 requests for telemedicine abortion received by WoW between March 2020-March 2021. Semi-structured interviews were conducted with eight healthcare professionals involved in the provision of abortion services to explore how they perceive of women's access to abortion services in Germany during the pandemic.
Study design A mixed-methods study design was used to gain an in-depth understanding of the impact of the pandemic on access to abortion services in Germany. The quantitative data was obtained from WoW in the beginning of April 2021. To complement the quantitative data, the perspectives of German healthcare professionals (HPs) were gathered through semi-structured interviews between April and June 2021. We used methodological triangulation to increase reliability. The quantitative and qualitative results were compared to nuance findings and to identify contradictions.