The challenge was to code a set of highly variable partnerships in a manner that would both effectively describe this diversity and generate categories that could meaningfully group partnerships together.
It is evident that all the variables except Partnership Engagement play a role of their own (Table 2).
In addition to age and sex, we included functional limitations, education and a variable measuring respondents' partnership history.
Often cohabitation and marriage are combined into one partnership variable (e.g., Bartus et al. 2013; Begall and Mills 2012), or cohabiting individuals are included as unmarried (e.g., Köppen 2010; Kravdal 2007).
In order to find out which medical and sociodemographic factors have an impact on the distress of the patients (QSC-R23 total score), we looked at the variables age, gender, partnership, diagnoses, metastases (yes/no), illness duration and treatment setting.
We created a new variable of multiple partnerships with a value of "1" if a participant reported ≥ 2 sexual partners in the past 6 months, or "0" if having only one or no sexual partner.
Demographic variables included gender, age, partnership status (single, married, in a partnership), citizenship, mother tongue and education.
General practice based primary health care in the NHS is a prime example, where general practitioners are, in the main, owner-occupier partnerships, with variable levels of collaboration with other professions such as community nurses, midwives and others.
Response rate by partnership cohort was variable.
Sociodemographic variables collected were age, gender, partnership, level of education and body mass index (BMI).
