dataesr/scientific-paragraphs-categorization huggingface dataset:
Since this industry is non-tradables, the counterpart is government consumption on non-tradables. See  Cardi, Restout and Claeys [2020]  for more details. The advantage of this database over WIOD is that the sum of G T and G N is very close to government final consumption expenditure time series provided by the OECD that we use to identify shocks to government consumption in section 2 of the main text. Data coverage: COFOG allows us to construct time series (in domestic currency units) over 1995-2015 for 16 OECD countries as data are not available for Canada and they are too short for Japan. We find that on average, 91% of government consumption is spent on non-traded goods and 9% is spent on traded goods. The cross-country correlation between ω G N from COFOG and the intensity of the non-traded sector in government consumption from WIOD stands at 0.
36. Note that with COFOG, international differences in ω G N are small. Because WIOD provides data for demand components, it is more accurate than COFOG which gives information about the purpose of the government transaction only. By contrast, time series from WIOD display some significant variations over time while time series from COFOG are not characterized by abrupt changes. Therefore we use WIOD data to calculate the intensity of a sector in government spending and calibrate our model while we use COFOG dataset to run a time series analysis. In both cases, we find that time series are very close to the time series from OECD for government final consumption expenditure. A Shock to government consumption increases both traded and non-traded government spending. In Fig. 34 , we plot the dynamic responses of the traded and nontraded components of government consumption to an exogenous increase in government consumption by 1% of GDP.
Total hours worked, L it , are total hours worked by persons engaged (H EMP). By construction, total hours worked is the sum of traded and non-traded hours worked. Sources: EU KLEMS ([2011], [2017]) and OECD STAN ([2011], [2017]) databases. • Real consumption wage, W C,it = W it /P C,it , is constructed as the nominal aggregate wage divided by the consumer price index (CPI). Source: OECD Prices and Purchasing Power Parities Database [2017] for the consumer price index. The nominal aggregate wage is calculated by dividing labor compensation (LAB) by total hours worked by persons engaged (H EMP). Sources: EU KLEMS ([2011], [2017]) and OECD STAN ([2011], [2017]) databases. • Aggregate total factor productivity, TFP it , is constructed as the Solow residual from constant-price domestic currency series of GDP, capital, LIS s L,i , and total hours worked.
In Appendix E, we detail the procedure to construct time series for the aggregate capital stock. The aggregate LIS, s L , i, is the ratio of labor compensation (compensation of employees plus compensation of self-employed) (LAB) to GDP at current prices (VA) in sector averaged over the period 1970-2015 (except Japan: 1974-2015). Sources: EU KLEMS ([2011], [2017]) and OECD STAN ([2011], [2017]) databases. • Real gross domestic product, Y R,it , is the sum of traded and non-traded value added at constant prices. Sources: EU KLEMS ([2011], [2017]) and OECD STAN ([2011], [2017]) databases. • E Construction of Time Series for Capital-Utilization-Adjusted TFP at a Sectoral Level from Imbs's [1999] Method.
Q. 2 OECD [2021] TFP adjusted with Capital QualityWhile evidence for the United States from the Fernald's measure of technology corroborates our measure of efficiency, we use a different measure provided by the OECD[2021]. The OECD measure of TFP is based on the construction of time series for capital services which are computed separately for eight non-residential fixed assets, including computer hardware, telecommunications equipment, transport equipment, other machinery and equipment and weapons systems, non-residential construction, computer software and databases, R&D and other intellectual property products. The volume index of total capital services is computed by aggregating the volume change of capital services of all individual assets using a Törnqvist index that applies asset specific user cost shares as weights.
ScR standards. Data charting and qualitative content analysis directed by SEIPS will explore how the work system and its constituting elements have been described in the literature and identify specific gaps and opportunities for future research. Borrowing from realist approaches, included studies will be assessed in terms of richness and relevance to our review question. Discussion: Strengths of this scoping review include PPCI and a converging focus on medication safety, medication self-management and HFE. Ultimately, this approach will advance our understanding of this complex system and guide opportunities to broaden and strengthen the evidence base.
Methods Patient, public and carer involvement Patient, public and carer involvement (PPCI) in research is important to optimise the relevance and meaning of the research to those affected by the topic and to increase the uptake and translation of research into sustainable practice  21, 22. PPCI supports stakeholders, such as end users and professional or non-professional providers, to have parity with researchers. This helps stakeholders, patients and carers maintain a central status in the three domains of quality in healthcare, namely clinical effectiveness, patient safety and patient experience  23, 24. This scoping review will incorporate and report on PPCI in line with the Authors and Consumers Together Impacting on eVidencE (ACTIVE) framework  22. Contributors will be invited to participate on a continuous basis at the following stages: selection of studies, data collection, qualitative analysis, interpretation of findings, writing and publishing, knowledge translation and impact, including outreach activities and the co-design of lay summary materials.
Eligibility criteria The eligibility criteria were developed around a modified version of the PCC tool (participants, concept, context)  25  , presented below. Relevant peer-reviewed primary studies will be included while secondary studies will not, although references cited therein will be screened. This approach will help prevent inadvertent double counting of data originating from primary studies  25. There is no restriction on the date of study publication. We will attempt to translate studies published in languages other than English. Participants. Studies with a primary focus on lay participants such as patients and their non-professional carers (e. g. , family members or laypeople who may or may not have received training) will be included. Studies focusing on healthcare practitioners, formally employed caregivers or professional carers will be excluded.
Study design. Eligible studies will include those that analysed or presented qualitative data by considering principles of human factors or ergonomics, systems engineering, sociotechnical or socioecological systems, or other relevant theories or frameworks, which enabled the identification of at least one of the following concepts: work system elements (e. g. , people, environments, tasks, tools, technologies), work processes (e. g. , obtaining supply, administering, monitoring), work outcomes (e. g. , humanistic, organisational, clinical or economic) and factors (facilitators and barriers to safe and effective medicines management) associated with the work system.
Search strategy The search strategy, shown in Table  1 , was constructed for the MEDLINE and Embase databases using the Embase interface and was endorsed by a subject librarian. The systematic search will be tailored to the interface for PsycINFO, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science. Each search string will focus on four key concepts: 1) medication use; 2) by a population of patients or informal carers; 3) within non-formal settings; as reported by 4) studies.
Selection of studies Search results from each database will be uploaded to EndNote (Clarivate, London, UK) and de-duplicated. This body of citations will be imported to the Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia), where further de-duplication and screening will be performed. All reviewers will independently review a proportion of titles and abstracts to ensure a common understanding of the application of eligibility criteria. In round one, two reviewers will then independently screen each study by title and abstract. Selected studies from round one will advance to round two where two reviewers will independently screen the article full text to determine final eligibility. Where differences in opinion arise or when a decision cannot be made at either round one or two, consensus will be reached ideally by discussion or through arbitration by another reviewer.
g. , Microsoft Excel or NVivo (QSR International) will be used to support qualitative data analysis and management. Included studies will be assessed during data charting in terms of their richness (adequacy of data in the included studies) and relevance (the degree of fit of the included studies to our review questions). Borrowing from the realist paradigm and guidance for assessing the confidence in evidence from reviews of qualitative research  29  , a five-point richness scale will be used to estimate the depth of theoretical application of human factors theories and perspectives, and a five-point relevance scale will be used to determine how relevant each study is to our review objectives. This assessment of richness and relevance will complement the above-mentioned method in meeting the objective to identify research gaps and direct future studies.
Data collection Data were generated from a survey sent to a random sample of civil servants of Bhutan in various jobs, hierarchical levels, public agencies, and ministries. From Bhutan's population of 26,320 civil servants, a stratified random sample  (Johnson et al. , 2015)  of 4,840 civil servants stratified by their different occupational groups received the survey. Therefore, we contacted close to 20% of the whole population of Bhutan civil service. The responses were collected in one single wave. Respondents were contacted by emails. HR Managers in all Ministries were instructed about the survey and wrote to the staff under their supervision that some civil servants will be contacted for a survey based on a random sample. In some case, local HR Managers did oral presentation of the survey to the civil servants under their leadership.
Measurement model We assessed the measurement model via a confirmatory factor analysis. We ran the analysis on R 4. 1 (R Core Team, 2021) with the lavaan package  (Rosseel, 2012). The measurement model achieved good fit according to usual fit indices: χ 2 = 1131. 101; df = 335; p <. 001; CFI =. 94; TLI =. 93; NFI =. 91; RMSEA =. 057; SRMR =. 048. We then assessed the reliability and convergent/discriminant validities with the semTools package  (Jorgensen et al. , 2021). Table  1  shows the reliability and convergent validity for each concept. All the scales are reliable as the Cronbach's α are upper than 0. 7 and they reached convergent validity as the average variance extracted are above 0. 5. Table  2  shows the HTMT discriminant validity  (Henseler et al. , 2015). As the values are below 0.
Common method variance We finally assessed that common method variance is not an issue for our study  (Podsakoff et al. , 2003). We used the ConMET package (De Schutter, 2021) to test competitive models where items from two constructs load on the same latent variable. All the configurations significantly decreased the fit of measurement model (i. e. χ 2 significantly increase with p <. 001), as shown in Table  3. In addition, we tested the performance of the Harman's One Factor  (Harman, 1967). The results show that it performed poorly compared to the measurement model (p <. 001).
Hypotheses testing We estimated the structural model on R 4. 1 (R Core Team, 2021) with the lavaan package  (Rosseel, 2012). We report the results in Table  4. The direct-only effect of corruption on performance (Model 1) is negative and significant (β = -. 19, p <. 001). However, when integrated with job satisfaction (Model 2), this direct effect becomes non-significant (β = -. 01, p >. 05). This is partial support for a mediated-only effect, hence the additional mediation analysis in the next part. We use the full model (Model 3) to test the hypotheses. We reject H1 as the results show that the effect of corruption on performance is not significant (β =. 00, p >. 05). Conversely, we accept H2 because corruption significantly decreases job satisfaction (β = -. 23, p <. 001). In line with H3, the higher job satisfaction, the higher performance (β =.
Mediation analysis We ran a mediation analysis R 4. 1 (R Core Team, 2021) with the lavaan package  (Rosseel, 2012) , with 5,000 bootstrap samples  (Zhao et al. , 2010). The results (Table  5 ) show that all the indirect effects that run through job satisfaction to performance are significant as their 95% confidence intervals exclude zero. More precisely, (1) the indirect effect from corruption is negative and significant (β = -. 15, p <. 001, 99. 9% CI = [-. 265, -. 053]), (2) the indirect effect from promotional activities is positive and significant (β =. 05, p <. 05, 95% CI = [. 002,. 098]), (3) the indirect effect from performance management is positive and significant (β =. 47, p <. 001, 99. 9% CI = [. 222,. 873]), and (4) the indirect effect from autonomy is positive and significant (β =.
Post-Hoc power analysis To make sure that our results are robust, we ran a series of robustness checks. The post-hoc power analysis allows to know if the sample size is sufficient to provide robust estimates  (Moshagen & Erdfelder, 2016). We used the semPower package  (Jobst et al. , 2021)  to evaluate the power of our analysis. Given that the RMSEA is. 057, the sample size is 1338, the degrees-of-freedom are 335, and the alpha is. 05, the computation shows that the power is satisfactory (>. 999). Figure  2  shows the associated central and non-central χ 2 distributions.
Acknowledgements This article is based on research supported by a grant from the  Swiss Agency for Development and Cooperation (SDC) , under the supervision of the  Anti-Corruption Commission of Bhutan  and in collaboration with the  Royal Institute of Management (Bhutan)  and  Audencia Business School (France). The authors thank  Professor Andy Crane  and  Professor Stephen Gates  for their comments on earlier versions of this article. Only the authors are responsible for all interpretations and conclusions as well as for all remaining errors and omissions.
Analysis of the management of shortages in the availability of health products in France Mapping the risk of shortages within the health products supply chain Figure  1  depicts a mapping of the supply chain and identifies the main causes of shortages. The map was carried out, following stakeholder exchanges, using Miro software, version 0. 7. 37. This overview provides insight into the extent of action required and emphasizes the involvement of various stakeholders across the health products supply chain: industrials at the manufacturing level, regulators, and other stakeholders impacting the market. The identified causes are categorized according to the different stages of the supply chain. These include weaknesses in the production chain, such as insufficient supply, manufacturing failures, and non-compliance with regulatory requirements.
Data from the French health products shortage notification system Our observational retrospective study analyzed reports of health products shortages from the French national system. In France, the Public Health Code defines a shortage as the impossibility of manufacturing or using a medicine (CSP Art. R. 5124-49-1). Pharmaceutical companies, which are responsible for ensuring the availability of the medicines they market in France, notify ANSM of the risk of stock shortage or actual stock shortages. Since 2012, manufacturers of major therapeutic interest drugs have been required to promptly notify the ANSM of any potential or actual supply chain shortages that could lead to or have resulted in a shortage or to face financial penalties (Decrees 2012-1096 of 28th September 2012 and 2016-993 of 20th July 2016).
The reporting system database contains information reported by Marketing Authorization Holder (MAH) through completed declaration forms. The following data were analyzed over a ten-year period between 2013 and 2022: number of reports, type of declaration (shortage or shortage risk), and ATC codes of the major therapeutic interest drug concerned (according to the WHO Collaborating Centre for Drug Statistics Methodology)  (27). The causes for major therapeutic interest drugs have been reported since 2021 from reports declared in the new e-portal (Trustmed), and they were analyzed over the year 2022. For medical devices, reports of risks of shortages or disruptions in availability were received following the implementation of a non-mandatory procedure specific to medical devices established by ANSM, and analyzed over an 18-month period from 1 st March 2022 to 31 st August 2023.
Strengthening European cooperation and regulation to better prevent health product shortages A strategy to secure the supply of health products is being implemented at the European level  (16, 23, (30) (31) (32) (33). To reinforce the EMA role in crisis preparedness and management for health products, a new regulation has been implemented in 2022  (25). This could also provide a framework for dealing with shortages more broadly. A comprehensive crisis management plan also covers medical devices, which have not been taken into account at EMA level until now. A network of contact points was created (SPOC), and European working groups were set-up: the  CHESSMEN   The establishment of a database on drug supply within the EMA allows for the regulation and facilitation of data consolidation. With regard to medical devices, the ANSM supports common measures and positions, in particular with regard to the provisions adopted at national level.
Finally, the Global Surveillance and Monitoring System  [14] , through a capillary network of regulatory focal points, maintains a global database of SF products, issues alerts, and provides evidence-based trend analysis. Of sixty alerts published to date, 19 concern 56 products identified in eight Francophone African countries (Cameroon, Cote d'Ivoire, Togo, Niger, Burkina Faso, CAR, Chad, Mali)  [15]. However, reporting remains low, due to various barriers, including limited awareness, poor detection capacity, inefficient information systems, and concerns over reputational damage.
Procurement agencies Procurement agencies may engage in surveillance. The Ecumenical Pharmaceutical Network (EPN), an independent, non-profit, Christian organisation committed to provide quality-assured pharmaceutical services, started in 2010 the Minilab project  [20] , in cooperation with the German Institute for Medical Mission, WHO, the Mission for Essential Drugs and Supplies (Kenya)  [21] , and the University of Tubingen (Germany). Nine out of nineteen members of the EPN Minilab Network are in Francophone Africa. Focal points at EPN partner organizations are trained to perform a visual inspection of incoming medicines  [22] , followed by simplified disintegration tests and thin-layer chromatography analyses with Minilab ™. As EPN partners are frequently located in rural, hard-to-reach areas, their data can integrate the national post-marketing surveillance of NRAs.
Acknowledgements The authors would like to acknowledge all the participants to the meeting "Où en sommes-nous dans la  lutte contre les médicaments de qualité inférieure et falsifiés dans les  pays d' Afrique francophone ? Comment  mobiliser les gouvernements et les acteurs nationaux autour  de cette question importante ?" (1 December 2022), which inspired this short report, for sharing their views and perspectives, and engaging in fruitful discussions on SF medicines in Francophone African countries. They also want to thank  Magalie Schotte  and  Nathalie Brouwers  of  Because Health , for their continuous support.
Further, we aim to articulate the inter-country compossibility of these patterns; that is, we highlight their "structural coupling, co-evolution and mutual complementarities-exclusivities and their impact on differential accumulation at a world scale"  (Jessop, 2014, p. 54). Empirically, these different patterns are identified via a geometric data analysis for 133 countries in 1995 and 2015. We use data from well-established international databases along six dimensions (GVC participation, GVC value capture, investment, socioeconomic development, domestic environmental impact and international environmental balance). The rest of the paper is structured as follows: section 2 reviews the literature, acknowledging the limited engagement of the GVCs literature with ecological issues at the macro level.
, 2021; Frey, 2019). In the part of this literature that is directly linked to GVCs, studies have empirically sought to establish a link between trade, income growth and decline of some specific environmental impacts. Wang et al. (2019)  estimate the effect of participation in GVCs on per capita CO2 emissions, using panel data from 1995-2011 for 62 countries. The authors find that participation in GVCs follows the hypothesized inverted U-shaped relationship with per capita CO2 emissions. However, per capita GDP showed an N-shaped relationship with per capita CO2 emissions, implying that after a given level of income, emissions begin rising again. Their study suggests that greater participation in GVCs and upgrading through investments in R&D can technically reduce carbon emissions within a nation's borders, yet this relationship may only hold up to a certain threshold.
These variables account for the main aspects of the ecological footprint of economic activities. We approach countries' ecological footprint in two ways, reflecting our two ecological dimensions. First, we measure environmental impacts domestically, through production-based environmental variables. Second, we include an international environmental balance -a consumption-based perspective -which accounts for the environmental content embodied in net imports  (Peters, 2008; Piñero et al. , 2019b). As such, we include a total of 8 ecological variables. We collected data for 133 countries for these 16 variables for the years 1995 and 2015. While some variables (e. g. , the employment rate) were retrieved from a single source, others (e. g. , GVC participation) were built using multiple sources.
Before performing our statistical analysis, we check whether our data are suited for factor analysis using the Keyser-Meyer-Ohlin (KMO) and Bartlett tests (see Appendix). We then analyse our dataset made of the six index variables using a principal component analysis and a hierarchical clustering. Performing the PCA before the clustering reduces the number of dimensions to be analysed by getting rid of the noise in our data and keeping only the statistically significant information. It therefore improves the quality and the stability of the clustering, which we perform on the principal components rather than on the raw data  (Husson et al. , 2010 (Husson et al. , , 2017)). 3 Our analysis indicates that at a 90% confidence level the first two axes are statistically significant and carry real information.
These axes present an amount of inertia of 53. 71%. This is higher than the reference value of 42. 71%, which is the 0. 9-quantile of the inertia percentages distribution obtained by simulating 100,000 normally distributed data tables of equivalent size. Since the first two axes carry an inertia just above 50% of the total inertia contained in our dataset, we nonetheless decide to keep the third principal component as well. This allows us to keep 70. 9% of the inertia while still removing a substantial amount of the statistical noise contained in the data. 4.