Socio demographic characteristics of patients and carers making the most frequent resolved financial claims (April 2009–March 2010).

*<p>DLA  =  Disability Living Allowance.</p>**<p>ESA  =  Employment Support Allowance (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042979#pone.0042979.s001" target="_blank">appendix S1</a>).</p

Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market

Background: One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product (GDP). This paper aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market by matching job seekers with vacant positions.Problem: Currently, human experts carry out this process; however, this is time-consuming and labour-intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate or analyse labour-market data, resulting in several inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour-market without a national data centre. Method: This paper proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research collaborators who are all professionals in Human Resources (HR). As this research is data-driven in nature, it requires collaboration from domain experts. Result: The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERT pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: Result evaluation: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 minutes, whereas human experts took more than six days on average. Implication: Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data.</p

Interview topic guides – clients.

<p>Interview topic guides – clients.</p

Addressing the Financial Consequences of Cancer: Qualitative Evaluation of a Welfare Rights Advice Service

<div><h3>Background</h3><p>The onset, treatment and trajectory of cancer is associated with financial stress among patients across a range of health and welfare systems and has been identified as a significant unmet need. Welfare rights advice can be delivered effectively in healthcare settings, has the potential to alleviate financial stress, but has not yet been evaluated. We present an evaluation of a welfare rights advice intervention designed to address the financial consequences of cancer.</p> <h3>Methods</h3><p>Descriptive study of welfare outcomes among 533 male and 641 female cancer patients and carers aged 4–95 (mean 62) years, who accessed the welfare rights advice service in North East England between April 2009 and March 2010; and qualitative interview study of a maximum variation sample of 35 patients and 9 carers.</p> <h3>Results</h3><p>Over two thirds of cancer patients and carers came from areas of high socio-economic deprivation. Welfare benefit claims were successful for 96% of claims made and resulted in a median increase in weekly income of £70.30 ($109.74, €84.44). Thirty-four different types of benefits or grants were awarded. Additional resources were perceived to lessen the impact of lost earnings, help offset costs associated with cancer, reduce stress and anxiety and increase ability to maintain independence and capacity to engage in daily activities, all of which were perceived to impact positively on well-being and quality of life. Key barriers to accessing benefit entitlements were knowledge, system complexity, eligibility concerns and assumptions that health professionals would alert patients to entitlements.</p> <h3>Conclusions</h3><p>The intervention proved feasible, effectively increased income for cancer patients and was highly valued. Addressing the financial sequelae of cancer can have positive social and psychological consequences that could significantly enhance effective clinical management and suitable services should be routinely available. Further research is needed to evaluate health outcomes definitely and assess cost-effectiveness.</p> </div

Overview of local health trainer models.

<p>Research sites (A, B and C) were chosen to reflect the heterogeneity in local delivery models. The sites are not named to preserve the participants' anonymity.</p

Participant Identifiers.

<p>Participant Identifiers.</p

Interview topic guides – staff.

<p>Interview topic guides – staff.</p

Social and demographic characteristics of 1174 individuals accessing Macmillan welfare rights advice service (April 2009–March 2010).

<p>Social and demographic characteristics of 1174 individuals accessing Macmillan welfare rights advice service (April 2009–March 2010).</p

Features of the client sample.

<p>Features of the client sample.</p

Demographic factors and cancer type of interview sample.^*

*<p>Demographic information collected for 35 interviewees and one carer.</p