Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

There has been considerable recent interest in methods of determining sample size for qualitative research a priori, rather than through an adaptive approach such as saturation. Extending previous literature in this area, we identify four distinct approaches to determining sample size in this way: rules of thumb, conceptual models, numerical guidelines derived from empirical studies, and statistical formulae. Through critical discussion of these approaches, we argue that each embodies one or more questionable philosophical or methodological assumptions, namely: a naïve realist ontology; a focus on themes as enumerable 'instances', rather than in more conceptual terms; an incompatibility with an inductive approach to analysis; inappropriate statistical assumptions in the use of formulae; and an unwarranted assumption of generality across qualitative methods. We conclude that, whilst meeting certain practical demands, determining qualitative sample size a priori is an inherently problematic approach, especially in more interpretive models of qualitative research.

Anonymising qualitative research data can be challenging, especially in highly sensitive contexts such as catastrophic brain injury and end-of-life decision-making. Using examples from in-depth interviews with family members of people in vegetative and minimally conscious states, this article discusses the issues we faced in trying to maximise participant anonymity alongside maintaining the integrity of our data. We discuss how we developed elaborate, context-sensitive strategies to try to preserve the richness of the interview material wherever possible while also protecting participants. This discussion of the practical and ethical details of anonymising is designed to add to the largely theoretical literature on this topic and to be of illustrative use to other researchers confronting similar dilemmas.

Background: Musculoskeletal (MSK) pain from the five most common presentations to primary care (back, neck, shoulder, knee or multi-site pain), where the majority of patients are managed, is a costly global health challenge. At present, first-line decision-making is based on clinical reasoning and stratified models of care have only been tested in patients with low back pain. We therefore, examined the feasibility of; a) a future definitive cluster randomised controlled trial (RCT), and b) General Practitioners (GPs) providing stratified care at the point-ofconsultation for these five most common MSK pain presentations. Methods:The design was a pragmatic pilot, two parallel-arm (stratified versus non-stratified care), cluster RCT and the setting was 8 UK GP practices (4 intervention, 4 control) with randomisation (stratified by practice size) and blinding of trial statistician and outcome data-collectors. Participants were adult consulters with MSK pain without indicators of serious pathologies, urgent medical needs, or vulnerabilities. Potential participant records were tagged and individuals sent postal invitations using a GP point-of-consultation electronic medical record (EMR) template. The intervention was supported by the EMR template housing the Keele STarT MSK Tool (to stratify into low, medium and high-risk prognostic subgroups of persistent pain and disability) and recommended matched treatment options. Feasibility outcomes included exploration of recruitment and follow-up rates, selection bias, and GP intervention fidelity. To capture recommended outcomes including pain and function, participants completed an initial questionnaire, brief monthly questionnaire (postal or SMS), and 6-month follow-up questionnaire. An anonymised EMR audit described GP decision-making.Results: GPs screened 3063 patients (intervention = 1591, control = 1472), completed the EMR template with 1237 eligible patients (intervention = 513, control = 724) and 524 participants (42%) consented to data collection (intervention = 231, control = 293). Recruitment took 28 weeks (target 12 weeks) with > 90% follow-up retention (target > 75%). We detected no selection bias of concern and no harms identified. GP stratification tool fidelity failed to achieve a-priori success criteria, whilst fidelity to the matched treatments achieved "complete success". Conclusions: A future definitive cluster RCT of stratified care for MSK pain is feasible and is underway, following key amendments including a clinician-completed version of the stratification tool and refinements to recommended matched treatments.