Thematic analysis is frequently used to analyse qualitative data in psychology, healthcare, social research and beyond. An important stage in planning a study is determining how large a sample size may be required, however current guidelines for thematic analysis are varied, ranging from around 2 to over 400 and it is unclear how to choose a value from the space in between. Some guidance can also not be applied prospectively. This paper introduces a tool to help users think about what would be a useful sample size for their particular context when investigating patterns across participants. The calculation depends on (a) the expected population theme prevalence of the least prevalent theme, derived either from prior knowledge or based on the prevalence of the rarest themes considered worth uncovering, e.g. 1 in 10, 1 in 100; (b) the number of desired instances of the theme; and (c) the power of the study. An adequately powered study will have a high likelihood of finding sufficient themes of the desired prevalence. This calculation can then be used alongside other considerations. We illustrate how to use the method to calculate sample size before starting a study and achieved power given a sample size, providing tables of answers and code for use in the free software, R. Sample sizes are comparable to those found in the literature, for example to have 80% power to detect two instances of a theme with 10% prevalence, 29 participants are required. Increasing power, increasing the number of instances or decreasing prevalence increases the sample size needed. We do not propose this as a ritualistic requirement for study design, but rather as a pragmatic supporting tool to help plan studies using thematic analysis.

Purpose The number of cancer survivors has risen substantially due to improvements in early diagnosis and treatment. Health behaviours such as physical activity (PA) and diet can reduce recurrence and mortality, and alleviate negative consequences of cancer and treatments. Digital behaviour change interventions (DBCIs) have the potential to reach large numbers of cancer survivors. Methods We conducted a systematic review and meta-analyses of relevant studies identified by a search of Medline, EMBASE, PubMed and CINAHL. Studies which assessed a DBCI with measures of PA, diet and/or sedentary behaviour were included. Results Fifteen studies were identified. Random effects meta-analyses showed significant improvements in moderate-vigorous PA (seven studies; mean difference (MD) = 41 min per week; 95% CI 12, 71) and body mass index (BMI)/weight (standardised mean difference (SMD) = −0.23; 95% CI −0.41, −0.05). There was a trend towards significance for reduced fatigue and no significant change in cancer-specific measures of quality of life (QoL). Narrative synthesis revealed mixed evidence for effects on diet, generic QoL measures and self-efficacy and no evidence of an effect on mental health. Two studies suggested improved sleep quality. Conclusions DBCIs may improve PA and BMI among cancer survivors, and there is mixed evidence for diet. The number of included studies is small, and risk of bias and heterogeneity was high. Future research should address these limitations with large, high-quality RCTs, with objective measures of PA and sedentary time. Implications for cancer survivors Digital technologies offer a promising approach to encourage health behaviour change among cancer survivors.

Objective To determine whether the ethnicity of UK trained doctors and medical students is related to their academic performance.Design Systematic review and meta-analysis.Data sources Online databases PubMed, Scopus, and ERIC; Google and Google Scholar; personal knowledge; backwards and forwards citations; specific searches of medical education journals and medical education conference abstracts.Study selection The included quantitative reports measured the performance of medical students or UK trained doctors from different ethnic groups in undergraduate or postgraduate assessments. Exclusions were non-UK assessments, only non-UK trained candidates, only self reported assessment data, only dropouts or another non-academic variable, obvious sampling bias, or insufficient details of ethnicity or outcomes.Results 23 reports comparing the academic performance of medical students and doctors from different ethnic groups were included. Meta-analyses of effects from 22 reports (n=23 742) indicated candidates of “non-white” ethnicity underperformed compared with white candidates (Cohen’s d=−0.42, 95% confidence interval −0.50 to −0.34; P<0.001). Effects in the same direction and of similar magnitude were found in meta-analyses of undergraduate assessments only, postgraduate assessments only, machine marked written assessments only, practical clinical assessments only, assessments with pass/fail outcomes only, assessments with continuous outcomes only, and in a meta-analysis of white v Asian candidates only. Heterogeneity was present in all meta-analyses.Conclusion Ethnic differences in academic performance are widespread across different medical schools, different types of exam, and in undergraduates and postgraduates. They have persisted for many years and cannot be dismissed as atypical or local problems. We need to recognise this as an issue that probably affects all of UK medical and higher education. More detailed information to track the problem as well as further research into its causes is required. Such actions are necessary to ensure a fair and just method of training and of assessing current and future doctors.

Objective To evaluate a national programme to develop and implement centrally stored electronic summaries of patients’ medical records. Design Mixed-method, multilevel case study. Setting English National Health Service 2007-10. The summary care record (SCR) was introduced as part of the National Programme for Information Technology. This evaluation of the SCR considered it in the context of national policy and its frontline implementation and use in three districts. Participants and methods Quantitative data (cumulative records created nationally plus a dataset of 416 325 encounters in participating primary care out-of-hours and walk-in centres) were analysed statistically. Qualitative data (140 interviews including policy makers, managers, clinicians, and software suppliers; 2000 pages of ethnographic field notes including observation of 214 clinical consultations; and 3000 pages of documents) were analysed thematically and interpretively. Results Creating individual SCRs and supporting their adoption and use was a complex, technically challenging, and labour intensive process that occurred more slowly than planned. By early 2010, 1.5 million such records had been created. In participating primary care out-of-hours and walk-in centres, an SCR was accessed in 4% of all encounters and in 21% of encounters where one was available; these figures were rising in some but not all sites. The main determinant of SCR access was the identity of the clinician: individual clinicians accessed available SCRs between 0 and 84% of the time. When accessed, an SCR seemed to support better quality care and increase clinician confidence in some encounters. There was no direct evidence of improved safety, but findings were consistent with a rare but important positive impact on preventing medication errors. SCRs sometimes contained incomplete or inaccurate data, but clinicians drew judiciously on these data along with other sources. SCR use was not associated with shorter consultations or reduction in onward referral. Successful introduction of SCRs depended on interaction between multiple stakeholders from different worlds (clinical, political, technical, commercial) with different values, priorities, and ways of working. The programme’s fortunes seemed to turn on the ability of change agents to bridge these different institutional worlds, align their conflicting logics, and mobilise implementation effort. Conclusions Benefits of centrally stored electronic summary records seem more subtle and contingent than many stakeholders anticipated, and clinicians may not access them. Complex interdependencies, inherent tensions, and high implementation workload should be expected when they are introduced on a national scale.