CESSDA Data Catalogue OAI-PMH Repository
Not a member yet
32838 research outputs found
Sort by
Coded Submissions and Publications (from the Project <i>"Opening the File Drawer: Assessing and Understanding Publication Bias in the Social, Economic, and Behavioral Sciences" </i>)
No full textThe project “Opening the File Drawer: Assessing and Understanding Publication Bias in the Social, Economic, and Behavioral Sciences” aims to assess the prevalence of publication bias and to identify its risk factors. Our interest lies in understanding the decision-making processes that lead researchers to selectively publish certain results, while others remain in the “file drawer.” Specifically, we examined successful study submissions by external researchers to two German probabilistic panels (the GESIS Panel and SOEP-IS), and analyzed discrepancies between, among other things, the research questions and hypotheses stated in the original study submissions and how they were presented in the subsequent publication(s). In a first step, we collected detailed information on the study submissions, the publications, and the authors. In a second step, we conducted an author survey among the submission authors. The present dataset contains the coding dataset – that is, all information extracted from the study submissions and the corresponding publications. The following six files are published here: – PubBias_Analysis dataset.dta (i.e., the final analysis dataset) – PubBias_Analysis dataset_Codebook.xlsx (i.e., the codebook for the analysis dataset) – PubBias_Coding scheme.xlsx (i.e., the coding scheme used to extract information from the submission documents and corresponding publications) – PubBias_Data preparation code.do (i.e., the data preparation code that converts the raw dataset into the analysis dataset) – PubBias_Data preparation_Log file.log (i.e., the log file containing the output of the data preparation) – PubBias_Raw dataset.xlsx (i.e., the raw dataset derived from coding of the submission documents and corresponding publications) The project “Opening the File Drawer: Assessing and Understanding Publication Bias in the Social, Economic, and Behavioral Sciences” aims to assess the prevalence of publication bias and to identify its risk factors. Our interest lies in understanding the decision-making processes that lead researchers to selectively publish certain results, while others remain in the “file drawer.” Specifically, we examined successful study submissions by external researchers to two German probabilistic panels (the GESIS Panel and SOEP-IS), and analyzed discrepancies between, among other things, the research questions and hypotheses stated in the original study submissions and how they were presented in the subsequent publication(s). In a first step, we collected detailed information on the study submissions, the publications, and the authors. In a second step, we conducted an author survey among the submission authors. The present dataset contains the coding dataset – that is, all information extracted from the study submissions and the corresponding publications. The following six files are published here: – PubBias_Analysis dataset.dta (i.e., the final analysis dataset) – PubBias_Analysis dataset_Codebook.xlsx (i.e., the codebook for the analysis dataset) – PubBias_Coding scheme.xlsx (i.e., the coding scheme used to extract information from the submission documents and corresponding publications) – PubBias_Data preparation code.do (i.e., the data preparation code that converts the raw dataset into the analysis dataset) – PubBias_Data preparation_Log file.log (i.e., the log file containing the output of the data preparation) – PubBias_Raw dataset.xlsx (i.e., the raw dataset derived from coding of the submission documents and corresponding publications) </p
Verhaltensintervention für insektenfreundliche Einstellungen und Verhalten
No full textDie Studie untersuchte die Wirksamkeit einer Intervention zur Förderung insektenfreundlichen Verhaltens mit einem quasi-experimentellen Design anhand von Online-Umfragen vor und nach der Intervention sowie einer Kontrollgruppe (N = 1.124). Ziel war es, die Veränderungen im Zeitraum von fünf Monaten in Bezug auf Einstellungen zu Insekten, Problembewusstsein, Wissen, Selbstwirksamkeit und Verhalten zu bewerten und den Einfluss der Intervention von anderen Zeitfaktoren durch eine Kontrollgruppe zu unterscheiden. Zudem haben wir untersucht, ob das Ausgangsniveau vor der Intervention die Wirkung der Intervention moderierte.Die Studie untersuchte die Wirksamkeit einer Intervention zur Förderung insektenfreundlichen Verhaltens mit einem quasi-experimentellen Design anhand von Online-Umfragen vor und nach der Intervention sowie einer Kontrollgruppe (N = 1.124). Ziel war es, die Veränderungen im Zeitraum von fünf Monaten in Bezug auf Einstellungen zu Insekten, Problembewusstsein, Wissen, Selbstwirksamkeit und Verhalten zu bewerten und den Einfluss der Intervention von anderen Zeitfaktoren durch eine Kontrollgruppe zu unterscheiden. Zudem haben wir untersucht, ob das Ausgangsniveau vor der Intervention die Wirkung der Intervention moderierte
Access to Treatment and Waiting Times in Primary Oral Health Care: Managers 2021
No full textAineisto on osa kyselykokonaisuutta, jonka tavoitteena oli selvittää julkiseen suun perusterveydenhuoltoon pääsyn odotusaikoja. Kyselyt kerättiin kansalaisilta, hammashoitajilta sekä suun terveydenhuollon johtajilta. Tämä aineisto käsittelee suun terveydenhuollon johtajien kyselyä, jossa tarkoituksena on selvittää käsityksiä odotusaikatietojen tarpeesta ja hyödyntämisestä. Aluksi vastaajille esitettiin väittämiä Terveyden ja hyvinvoinnin laitoksen ylläpitämästä tietopalvelusta ja siellä julkaistuista suun terveydenhuollon odotusaikatiedoista. Kysyttiin esimerkiksi, onko käyttänyt tietopalvelua, millaiseksi tietopalvelun kokee sekä mahdollistaako tietopalvelu odotusaikatietojen vertailun. Seuraavaksi kysyttiin, onko odotusaikatiedoista ollut hyötyä oman yksikön johtamisessa, omavalvonnassa ja vertailussa muihin yksiköihin. Lisäksi selvitettiin, ovatko tiedot vaikuttaneet yksikön hoidon tarpeen arvion kirjaamiskäytäntöjen arviointiin sekä ovatko odotusaikatiedot oman yksikön osalta paikkansapitäviä. Tämän jälkeen esitettiin väittämiä liittyen potilastietojärjestelmään. Kysyttiin muun muassa onko potilastietojärjestelmä vastaajien mielestä hyödyllinen hoidon tarpeen arvioinneissa sekä onko perehdyttämistä, ohjeita ja käytöntukea järjestelmän käyttöön lisätty. Lisäksi kysyttiin odotusaikatietojen hyödyntämisestä hoitoon pääsyn nopeuttamiseen ja tulisiko odotusaikatietojen esittämistä monipuolistaa. Lopuksi pyydettiin vielä arvioimaan, uskovatko vastaajat asiakkaiden tietävän, että internetissä julkaistaan odotusaikatietoja sekä kuinka moni pitää hyödyllisenä näiden tietojen julkaisemista. Taustamuuttujia ovat ikä, sukupuoli, tutkinto, tieto kuinka kauan toiminut johtotehtävissä sekä johdetun terveyskeskuksen väestöpohja.The data is part of a set of surveys aimed at determining waiting times for access to public primary oral health care. The surveys were collected from citizens, dental nurses, and oral health care managers. This material deals with the survey of oral health care managers, which aims to explore perceptions of the need for and use of waiting time information. First, respondents were presented with statements about the information service maintained by the Finnish Institute for Health and Welfare and the information on waiting times for dental care published there. They were asked, for example, whether they had used the information service, how they felt about it, and whether it enabled them to compare waiting time data. Next, they were asked whether the waiting time information had been useful in managing their own unit, in self-monitoring, and in comparing their unit with others. In addition, it was investigated whether the information had influenced the assessment of the unit's recording practices for assessing the need for treatment and whether the waiting time information was accurate for their own unit. After this, questions were asked about the patient information system. Among other things, respondents were asked whether they considered the patient information system useful in assessing the need for treatment and whether training, instructions, and user support for the system had been increased. In addition, questions were asked about the use of waiting time information to speed up access to care and whether the presentation of waiting time information should be diversified. Finally, respondents were asked to assess whether they believed that customers were aware that waiting time information was published on the internet and how many considered the publication of this information to be useful. Background information include age, gender, degree, length of time in management positions, and the population base of the health center managed
Growing Up in Scotland: Cohort 1: Sweep 11, 2021-2023, Attainment Data: Secure Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Growing Up in Scotland (GUS) study is a large-scale longitudinal social survey which follows the lives of several groups of Scottish children from infancy through childhood and adolescence, and aims to provide important new information on children and their families in Scotland. The study forms a central part of the Scottish Government's strategy for the long-term monitoring and evaluation of its policies for children, with a specific focus on the early years. Unlike other similar cohort studies, this survey has a specifically Scottish focus. A key objective of GUS is to address a significant gap in the evidence base for early years policy monitoring and evaluation. The study seeks both to describe the characteristics, circumstances and experiences of children in their early years (and their parents) in Scotland and, through its longitudinal design, to generate a better understanding of how children's start in life can shape their longer term prospects and development.Since 2005, study design and data collection have been undertaken by ScotCen Social Research with collaborations with the Centre for Research on Families and Relationships, based at the University of Edinburgh and the MRC/CSO Social and Public Health Sciences Unit over certain periods of the project. The survey design consisted of recruiting an initial total of 8,000 parents in 2005, comprising two cohorts of children (5,000 from birth, 3,000 from age two years and ten months), and then interviewing parents annually until their child reached age five years ten months. Further fieldwork was undertaken with the birth cohort when the children were around eight, ten, twelve and fourteen years old. A boost sample of 500 children from predominantly high deprivation areas was added to the cohort as part of the age 12 fieldwork.For sweeps 1 to 9 data were collected via an in-home, face-to-face interview with self-complete sections. Fieldwork for sweeps 10 and 11 were disrupted due to the COVID pandemic. As a result, portions of the data were collected via web and telephone questionnaires whilst others involved face-to-face interviews where they were permitted. The study user guides provide further details.Special Licence data:The main survey data are available under Special Licence:SNs 9373-9383 and 9386-9387 - Growing Up in Scotland: Cohort 1SN 7432 - Growing Up in Scotland: Cohort 2SN 8366 - Growing Up in Scotland: Cohort 1, Primary 6 Teacher SurveySecure Access Geographic Data:Geographic data are available under Secure Access and are separated by cohort, sweep and type of geographic variable. Information is available on the GUS Access Data web page. Users must also include the main Growing Up in Scotland Special Licence data in the Accredited Researcher Proposal form and add it to their projects (please note there is no need for Secure Access users to complete a separate Special Licence application).Secure Access Early Learning and Childcare Administrative Data:Care Inspectorate quality information on the settings which provided children in Birth Cohort 1 and Birth Cohort 2 with their state-funded early learning and childcare (pre-school) entitlement when they were aged between 3 and 5 years old is available under SN 8543 (Birth Cohort 1) and SN 8544 (Birth Cohort 2).Secure Access Linked Administrative Data:A data matching exercise was was undertaken using the Scottish Government Pupil Census at Birth Cohort 1 Sweep 11 and participants were linked with their Scottish Candidate Number (SCN). The SCNs were then supplied to the Scottish Qualifications Authority (SQA), who were able to provide the attainment records for participants (available under SN 9447). The SCNs were then supplied to Skills Development Scotland (SDS), who were able to provide the school leaver destinations record for participants (available under SN 9448).SN 9447 - Growing Up in Scotland: Cohort 1: Sweep 11, 2021-2023, Attainment Data: Secure AccessUsing GUS participants date of birth, postcode and school SEED code, a data matching exercise was undertaken using the Scottish Government Pupil Census. Using these identifiers, participants were linked with their Scottish Candidate Number (SCN). SCNs are allocated to pupils at school and in further-education colleges who undertake Scottish Qualifications Authority (SQA) courses. Once the matching exercise was complete, over 90% of Sweep 11 GUS participants were matched with their SCN. The pupil census only includes pupils at Local Authority funded schools in Scotland. Therefore, of those that could not be matched, the majority are most likely pupils at independent or private schools. The SCNs were then supplied to the SQA, who were able to provide the attainment record for participants. SCNs have since been removed from the dataset, and replaced with GUSID. Researchers can use this GUSID to link attainment data with GUS survey responses.When researchers are approved/accredited to access this study, the GUS Cohort 1, Sweep 11 study (SN 9383) will be automatically provided alongside.Main Topics:The data file includes an ID variable for matching to the main GUS sweep survey data and seven SQA attainment variables detailing information about the qualifications obtained. </div
National Child Development Study: Polygenic Indices, 2002-2004: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan. The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565. Survey and Biomeasures Data (GN 33004):To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412). A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.Linked Geographical Data (GN 33497): A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. Linked Administrative Data (GN 33396):A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.Multi-omics Data and Risk Scores Data (GN 33592)Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.Additional Sub-Studies (GN 33562):In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.SN 9440 - National Child Development Study: Polygenic Indices, 2002-2004: Special Licence AccessPolygenic indices (PGIs) aggregate Genome-wide Association Studies (GWAS) estimates across all measured single nucleotide polymorphisms (SNPs) to provide a single estimate of an individual’s genetic predisposition towards the trait under study. As such, SNPs can be considered as the building blocks of PGIs. It is important to note that the genetic predisposition represented by PGI is known inasmuch as it has been estimated accurately and reliably from a GWAS; not all SNPs are included in the GWAS or the GWAS estimates for a given SNP are inaccurate, then the genetic predisposition represented by the PGI will be lower than the true genetic predisposition.The PGIs have been developed using a consistent methodology that has been applied to harmonised genetic data across each cohort, enabling researchers to engage in consistent cross-cohort analysis for using derived genetic measures for the first time. All PGIs have been derived from large scale Genome-wide Association Studies (GWAS) with publicly available summary statistics. This approach is hoped to enable and encourage wider use of the genetic data collected in these studies. High level guidance on the use and interpretation of PGIs is provided.The PGIs were also developed in a consistent manner in a birth cohort born in 1946 (MRC National Survey of Health and Development, 1946c), which can be obtained by separate application to the Unit for Lifelong Health and Ageing at UCL.Main Topics:Polygenic indices; polygenic scores; polygenic risk scores; genome-wide association studies; human genetics; anthropometrics; brain structure and cognition; health behaviours; mental health; personality; physical health; social outcomes. </p
Quarterly Labour Force Survey, Household Dataset, January - March, 2025
No full textAbstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.Household datasetsUp to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.Change to coding of missing values for household seriesFrom 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.Additional data derived from the QLFSThe Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.End User Licence and Secure Access QLFS Household datasetsUsers should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.Changes to variables in QLFS Household EUL datasetsIn order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.Review of imputation methods for LFS Household data - changes to missing valuesA review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders. Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.Main Topics:The LFS household datasets cover:characteristics of the household: number of people of working age; number of people over working age; number of children aged 0 to 4; number of children aged 5 to 15; number of dependent children (i.e. those in full-time education) aged 16 to 18economic activity in the household: number of people in employment; number of people in full-time employment; number of people in part-time employment; unemployed; economically inactive; students; sick or disabled; economically inactive but would like to work and are not seeking work because they do not believe there is work available ('discouraged workers'); care of dependants</ul
Young People and Gambling Survey, 2024
No full textAbstract copyright UK Data Service and data collection copyright owner.The aim of the Young People and Gambling Survey is to explore young people’s attitudes towards gambling and their participation in different types of gambling activities, designed to provide a means of tracking these perceptions and behaviours over time. The survey looks at those forms of gambling and gambling style games that children and young people legally take part in along with gambling on age restricted products. The 2024 research was conducted by Ipsos on behalf of the Gambling Commission. The study collected data from 3,869 pupils aged 11 to 17 years old across curriculum years 7 to 12 (S1 to S6 in Scotland) using the Ipsos Young People Omnibus. Pupils completed an online self-report survey in class. Data have been weighted to the known profile of the population in order to provide a representative sample.Main Topics:The following topics are covered:attitudes towards and exposure to gamblingexposure to gambling advertisingother activities and risk takinggames and gaming machinesNational Lottery playonline gamblingimpact of gambling on young peopleexperience of gambling</p
Cancer Research UK Local Cancer Awareness Measure: Leicester, Leicestershire and Rutland (LLR), April-August 2024: Special Licence Access
No full textAbstract copyright UK Data Service and data collection copyright owner.The Cancer Awareness Measure (CAM) was developed in 2007-8 to address the absence of a validated measure of general public awareness of cancer (Stubbings, S., Robb, K., Waller, J., Ramirez, A., Austoker, J., Macleod, U., Hion, S., and Wardle, J. (2009) 'Development of a measurement tool to assess public awareness of cancer', British Journal of Cancer, 101(2), S13-S17.).The survey includes measures of awareness of signs and symptoms of cancer, cancer risk factors, age-related risk, screening programmes and potential barriers to seeing the GP. Since then, Cancer Research UK (CRUK) has significantly revised and updated the survey, including a wider range of questions and collecting data online instead of face-to-face. In 2023-2024 Cancer Research UK ran two Local Cancer Awareness Measure Plus (CAM+) pilots, collecting data in two local regions (Greater Manchester and Leicester, Leicestershire and Rutland (LLR)) using both an online panel and community sampling to recruit participants. The Greater Manchester pilot Local CAM+ datasets are available under SNs 9342 and 9358.The LLR pilot Local CAM+ dataset does not include National CAM+ questions on alcohol consumption, physical activity, perception of health services capacity and closeness to cancer. However, it includes additional questions on possible facilitators for cancer screening attendance and willingness to travel for hospital tests.A End User Licence version of this study is available under SN 9343.Further information about the CAM+ may be found on the Cancer Research UK Cancer Awareness Measure Plus (CAM+) webpage.Main Topics:The CAM questionnaires address the following topics: public awareness of cancer symptoms public knowledge of cancer risk factors barriers and enablers to help seeking uptake of screening programmes barriers to cancer screening (cervical, breast and bowel)experience of breast and cervical cancer screening symptom experience co-morbiditiesperception of symptom seriousness help seeking behaviours including remote consultation and re-presentation perceptions of remote consultation demographic variables including health behaviours such as smoking and fruit and vegetable consumption.From 2021 onwards, the questionnaire also covers:impact of COVID-19 on help seeking behaviourperceptions of safety from COVID-19 in different medical settings.The Special Licence version of the CAM dataset includes more detailed data on symptoms, risk factors and reasons for delay. Users should check the standard End User Licence (EUL) version of the study to see whether it meets their research needs before making an application for the Special Licence version.<br
Nourishing Britain: a Political Manual for Improving the Nation's Health, 2023-2024
No full textAbstract copyright UK Data Service and data collection copyright owner.The Nourishing Britain study comprises 20 semi-structured interviews with UK prime ministers, health secretaries and other relevant senior ministers, as well as two regional mayors, all of whom who were in post or government between 1990 (when the first government obesity-reduction targets were being developed) and 2022 (the government before this project started). The larger aim of the project was to develop a political manual for current and future politicians on how to effectively navigate the politics of food-related health policy.Main Topics:The two overarching research questions the project sought to answer were: What barriers did senior politicians face in government when trying to pursue food-related health policies? And: What factors helped them overcome these?</ul
The Committee Membership Dataset (CMD)
No full textWe provide the Committee Membership Dataset (CMD), a major expansion of available legislative data. The CMD records all committee assignments for MPs in 14 countries (14,963 MPs in 260 parties) from 1989 to 2024, including positions held (e.g., member, chairperson), assignment dates, and committee policy areas. Harmonized MP and party identifiers allow linkage with other widely used datasets.We provide the Committee Membership Dataset (CMD), a major expansion of available legislative data. The CMD records all committee assignments for MPs in 14 countries (14,963 MPs in 260 parties) from 1989 to 2024, including positions held (e.g., member, chairperson), assignment dates, and committee policy areas. Harmonized MP and party identifiers allow linkage with other widely used datasets