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This study aims to explore people’s experiences of taking cannabis therapeutically and to gather some real-world evidence (RWE) about the products they were using, their efficacy and what kinds of positive or negative effect/s patients experienced. The focus of this discussion is the efficacy of cannabis for the participants in this study.

This was an exploratory study that used a mixed methods approach: a survey and semi-structured interviews. The data presented here focus on thematic analysis of five of the open-ended survey questions. Results from a purposive survey sample are also briefly reported. Interview data are not reported on here.

Across the sample (n = 213), 95.6% of participants reported that taking cannabis helped them with a number of conditions. The most common three themes across the thematic analysis were that cannabis helped with pain relief, sleep and anxiety. Negative effects, some of which related to having to source cannabis from the illicit market, were relatively minor and experienced by 28% (n = 58) of participants. An important finding was that 49% (n = 76) of those who said their use of prescribed medicines had decreased (n = 155), significantly decreased and in some cases stopped their use of prescribed medications.

This study reports on a sample of participants with clinically diagnosed conditions and adds to the RWE base about the efficacy of using cannabis for therapeutic purposes in the New Zealand context.

The aim of this paper is to demonstrate and discuss a number of child-centric research methods/stimuli involving young children (5-6 years old) in interviews without, and subsequently with their parents. Existing and new methods were selected and developed for a study which aimed at obtaining insights into parents’ and young children’s understandings of children’s influence and family interaction with regard to family food consumption practices.

A total of 35 children were interviewed using semi-structured interviews in five kindergartens. Subsequently, 13 families were interviewed in their homes. The latter interviews included the same children as were interviewed in the kindergarten. The methods discussed include drawings, a desert-island-choice task, a sentence completion task, photographs, vignettes and a video-clip.

When interviewing young children about family decision making influence, the use of engaging methods contributes to the quality of data achieved and to the participants’ enjoyment of their participation. Care should be taken not to overload children with exercises. Visual rather than verbal methods worked better for engaging the children in the research process; for parents all included methods worked well.

The current study shows that a method developed specifically for the study (desert-island-choice task) was apt at including all family members’ perspectives; future studies should develop methods that capture shared rather than individual experiences. The study was carried out in wealthy areas in Denmark. It would be highly relevant to broaden the sample to other socio-economic and cultural contexts.

The study is based on interviews with children usually deemed too young to interview. The contribution is novel methods that allow for studying the interaction between children and parents and that are not based on reading and writing skills to access the perspectives of 5-6-year old children. Precautions regarding using existing methods are offered.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning.

A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach.

The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes.

This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.