Basic personal values underlie and give coherence to political values: A cross national study in 15 countries

Do the political values of the general public form a coherent system? What might be the source of coherence? We view political values as expressions, in the political domain, of more basic personal values. Basic personal values (e.g., security, achievement, benevolence, hedonism) are organized on a circular continuum that reflects their conflicting and compatible motivations. We theorize that this circular motivational structure also gives coherence to political values. We assess this theorizing with data from 15 countries, using eight core political values (e.g., free enterprise, law and order) and ten basic personal values. We specify the underlying basic values expected to promote or oppose each political value. We offer different hypotheses for the 12 non-communist and three post-communist countries studied, where the political context suggests different meanings of a basic or political value. Correlation and regression analyses support almost all hypotheses. Moreover, basic values account for substantially more variance in political values than age, gender, education, and income. Multidimensional scaling analyses demonstrate graphically how the circular motivational continuum of basic personal values structures relations among core political values. This study strengthens the assumption that individual differences in basic personal values play a critical role in political thought.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.