Applied social science projects that involve many interviews produce a vast amount of data or text that is difficult to structure and analyze systematically. Computer-assisted qualitative data analysis software is too advanced and sophisticated when all we want is to sort and structure the text. A new method, using Microsoft Word and Excel, has been developed. The method produces a flexible Word document of interview data separated into logical chapters and subchapters. All text is coded, and the codes correspond with headings in the final document. Systematic manual coding ensures that all the content is coded, not just words or terms that are extracted from the text. After several years of using and refining the method, both in projects with relatively few interviews and in those with more than 100, I believe that the method is efficient when there are four or more interviews. The method is also suitable for coding and structuring answers to open-ended questions in Web-based surveys. The coding may be performed by a supervised research assistant or a multidisciplinary analytical team, depending on the complexity of the problem. The purpose of the method is not to quantify qualitative data but only to sort and structure large amounts of unstructured data. The method consists of 10 steps, screenshots of which are included in the paper.

Non-suicidal self-injury (NSSI) is a highly prevalent behavioural problem among people with mental illness, yet many fundamental aspects of NSSI remain unknown. We studied the prevalence of NSSI, and its relationship with suicide ideation (SI) and suicide attempts (SA) among adult psychiatric outpatients, with a special focus on patients with personality disorders compared with patients with other disorders. During a 14-day period, data were collected on all available patients in all outpatient psychiatric clinics in Norway. This national clinical unselected cross-sectional dataset from 23,124 outpatients was used to generate proportional Venn diagrams of the prevalence of NSSI, SI and SA and their co-occurrence over the last four weeks. Differences in the risk for these behaviours across diagnoses were tested, both with and without adjustments for demographic and socio-demographic characteristics. Over the previous four-week period, 8.1% of the patients had experienced at least one episode of NSSI, 17.3% had SI and 0.6% had made at least one SA. Among patients with NSSI, 27.8% had co-occurring SI, and among patients with SI, 13% had co-occurring NSSI. The prevalence of SA was more than seven times higher among patients with NSSI behaviour than among patients without NSSI behaviour. Patients with a diagnosis of personality disorder had a significantly higher prevalence of SI, NSSI, and NSSI with co-occurring SI, than all other diagnostic groups; however, they were not systematically different from patients with other diagnoses in their prevalence of NSSI without co-occurring SI. These findings remained statistically significant even when controlling for socio-demographic variables. The prevalence of recent NSSI is high in patients receiving outpatient psychiatric treatment in Norway. NSSI is significantly more prevalent in patients with personality disorders than in patients with other diagnoses, mainly due to the significantly higher prevalence of NSSI with co-occurring SI in patients with personality disorders. The co-occurrence of NSSI and SI is also prevalent in all diagnostic groups, but both NSSI and SI appear alone more often than together. The strong association between NSSI and SA calls for a more proactive focus on NSSI behaviour in mental health clinical settings as an important suicide preventive measure.

Background: Many studies have investigated how unemployment influences health, less attention has been paid to the reverse causal direction; how health may influence the risk of becoming unemployed. We prospectively investigated a wide range of health measures and subsequent risk of unemployment during 14 years of follow-up. Methods: Self-reported health data from 36 249 participants in the Norwegian HUNT2 Study (1995–1997) was linked by a personal identification number to the National Insurance Database (1992–2008). Exact dates of unemployment were available. Cox’s proportional hazard models were used to estimate hazard ratios (HR) for the association of unemployment with several health measures. Adjustment variables were age, gender, education, marital status, occupation, lifestyle and previous unemployment. Results: Compared to reporting no conditions/symptoms, having ≥3 chronic somatic conditions (HR 1.78, 95% CI 1.46–2.17) or high symptom levels of anxiety and depression (HR 1.57, 95% CI 1.35–1.83) increased the risk of subsequent unemployment substantially. Poor self-rated health (HR 1.36, 95% CI 1.24–1.51), insomnia (HR 1.19, 95% CI 1.09–1.32), gastrointestinal symptoms (HR 1.17, 95% CI 1.08–1.26), high alcohol consumption (HR 1.17, 95% CI 0.95–1.44) and problematic use of alcohol measured by the CAGE questionnaire (HR 1.32, 95% CI 1.17–1.48) were also associated with increased risk of unemployment. Conclusion: People with poor mental and physical health are at increased risk of job loss. This contributes to poor health amongst the unemployed and highlights the need for policy focus on the health and welfare of out of work individuals, including support preparing them for re-employment.

Background: Adverse media coverage of isolated incidents affects the public perception of the risk of violent behavior among people with mental illness. However, the risk of violence is studied most frequently among inpatients, which falsely exaggerates the prevalence of people with mental illness because the majority of individuals receive treatment as outpatients. Aim: To estimate the prevalence of the risk of violence among inpatients and outpatients in psychiatric treatment, as well as the associations with gender, age, socio-economic status and co-morbid substance use disorders in all major diagnostic categories. Methods: We conducted a national census of patients in specialist mental health services in Norway, which included 65% of all inpatients (N = 2,358) and 60% of all outpatients (N = 23,124). Results: The prevalence of the risk of violence was 32% among inpatients and 8% among outpatients, where 80% of the patients in specialist mental health services were outpatients. If we weight the prevalence rates accordingly, less than 2% of the patients in specialist mental health services had a high risk of violent behavior. Conclusions: The stigma attached to those with mental illness is not consistent with the absence or low to modest risk of violent behavior in 98% of the patient group. Substance use disorders must be given priority in the treatment of all patient groups. Mental health care in general and interventions that target violent behavior in particular should address the problems and needs of these patients better, especially those who are unemployed, have a low level of education and have a background of being a refugee or an immigrant.