In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Example Input: Paragraph: Background: The widespread nursing shortage is of concern in Mainland China and globally. Factors underpinning the increased mobility of the nursing workforce and their contribution to nurses' turnover thus merit attention. Understanding nurses' job satisfaction is important, as this is a key factor in nurses' turnover. Objective: The study aimed to explore nurses' views and experience regarding different components of their working lives in Mainland China. Methods: A cross-sectional survey design was selected and 512 nurses working in the medical and surgical departments in two teaching hospitals in Beijing completed questionnaires yielding a response rate of 81%. Results: More than half of nurses (53.7%; n ¼ 275) were satisfied or very satisfied with their jobs and 15% (n ¼ 77) felt moderate to extreme occupational stress. The majority of the sample reported a high level of organizational commitment (63.7%; n ¼ 326) and professional commitment (85.9%; n ¼ 440) and only 5.9% (n ¼ 30) and 10.0% (n ¼ 51), respectively reported role conflict and role ambiguity often or very often. Nurses with a diploma or associate degree reported greater professional commitment and a lower level of role conflict than those with a bachelor degree (po0.05), but there were no significant differences in job satisfaction, organizational commitment, occupational stress and role ambiguity by educational programme (p40.05). Conclusions: Hospital nurses' positive feelings regarding their working lives may be influenced by developments in the health care system and the nursing profession in Mainland China. Nurses' educational level is an influencing factor on nurses' views and experiences of their working lives with the findings suggesting the need to develop a clinical career ladder for nursing staff in Mainland China. r 
 Title: Job satisfaction and its related factors: A questionnaire survey of hospital nurses in Mainland China
Example Output: False

Example Input: Paragraph: Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. Despite substantial public health improvements in the last century, infectious diseases remain one of the leading causes of both morbidity and mortality 1,2 . When confronted with an infectious disease outbreak, public health officials typically work to control the outbreak by performing assessments, analyzing surveillance data, identifying resources and interacting with subject mater experts 2-4 . Control measures are then implemented based on the cumulative information collected. These approaches rely heavily on good surveillance systems, access to experts, and good intuition about which control measures to use. As such, they are largely subjective, time consuming, and the infrastructure required is often not present in high disease burden areas. Modeling is an attractive supplemental method because of the ability to estimate an outbreak's trajectory and the effects of possible control measures in a timely manner. Compartmental models are historically common; they divide individuals into categories based on their disease status. The most common variant is the SIR model, named after the categories used-"susceptible", "infectious" and "recovered". Models of this nature have small computational requirements, and are thus commonly used as first pass attempts to characterize outbreaks or infections quickly 5 . For example, after the sudden emergence of Severe Acute Respiratory Syndrome (SARS) in the early 2000s, researchers used modeling to characterize the virus' epidemiology. Several used compartmental models with control measures like quarantine or isolation in various settings (e.g., hospitals or cities) to describe effects of possible interventions 6-9 . Similar work exists for essentially all well known infectious diseases. For example, Mandal et al. provide a review of models used for malaria 10 and Bauch et al. explore model use with respect to SARS and other emerging infectious diseases 6 . Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. In contrast, agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks 11 . This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test 11 . These models further require computational resources inaccessible to an average health department. Both agent-based and compartmental models exhibit additional features that are problematic to wide-spread model adoption. The first is the focus on particular disease-location pairs. This emphasis precludes application to 
 Title: An approach to and web-based tool for infectious disease outbreak intervention analysis OPEN
Example Output: False

Example Input: Paragraph: 19 20 21 22 112 While we are pressed with immediacy of the COVID-19 pandemic, we also need to keep a long-term 113 view for the future of our specialty. The COVID-19 pandemic has impacted all parts of the world. Given 114 the recent history of other epidemics (for example SARS, H1N1, ebola, and MERS), it is unlikely that this 115 will be the last epidemic/pandemic crisis. This type of crisis requires allergy divisions and training 116 programs communicate by virtual global assemblies. Communication between program directors and 117 fellows across different programs can provide support and solutions, which can be adapted at each 118 division based on their needs and resources. The entire specialty should unite with the societies such as 
 Title: Journal Pre-proof Running a virtual allergy division and training program in the time of COVID-19 pandemic
Example Output:
False