Alicia O’Cathain

Bio: Alicia O’Cathain is an academic researcher from University of Sheffield. The author has contributed to research in topics: Population & Health care. The author has an hindex of 55, co-authored 186 publications receiving 16991 citations. Previous affiliations of Alicia O’Cathain include Doncaster Royal Infirmary & University of Nottingham.

Validating the SF-36 health survey questionnaire: new outcome measure for primary care.

Abstract: OBJECTIVES--To test the acceptability, validity, and reliability of the short form 36 health survey questionnaire (SF-36) and to compare it with the Nottingham health profile. DESIGN--Postal survey using a questionnaire booklet together with a letter from the general practitioner. Non-respondents received two reminders at two week intervals. The SF-36 questionnaire was retested on a subsample of respondents two weeks after the first mailing. SETTING--Two general practices in Sheffield. PATIENTS--1980 patients aged 16-74 years randomly selected from the two practice lists. MAIN OUTCOME MEASURES--Scores for each health dimension on the SF-36 questionnaire and the Nottingham health profile. Response to questions on recent use of health services and sociodemographic characteristics. RESULTS--The response rate for the SF-36 questionnaire was high (83%) and the rate of completion for each dimension was over 95%. Considerable evidence was found for the reliability of the SF-36 (Cronbach9s alpha greater than 0.85, reliability coefficient greater than 0.75 for all dimensions except social functioning) and for construct validity in terms of distinguishing between groups with expected health differences. The SF-36 was able to detect low levels of ill health in patients who had scored 0 (good health) on the Nottingham health profile. CONCLUSIONS--The SF-36 is a promising new instrument for measuring health perception in a general population. It is easy to use, acceptable to patients, and fulfils stringent criteria of reliability and validity. Its use in other contexts and with different disease groups requires further research.

Process evaluation of complex interventions: Medical Research Council guidance

Abstract: Process evaluation is an essential part of designing and testing complex interventions. New MRC guidance provides a framework for conducting and reporting process evaluation studies

The Mixed Methods Appraisal Tool (MMAT) Version 2018 for Information Professionals and Researchers.

Abstract: INTRODUCTION: Appraising the quality of studies included in systematic reviews combining qualitative and quantitative evidence is challenging. To address this challenge, a critical appraisal tool was developed: the Mixed Methods Appraisal Tool (MMAT). The aim of this paper is to present the enhancements made to the MMAT. DEVELOPMENT: The MMAT was initially developed in 2006 based on a literature review on systematic reviews combining qualitative and quantitative evidence. It was subject to pilot and interrater reliability testing. A revised version of the MMAT was developed in 2018 based on the results from usefulness testing, a literature review on critical appraisal tools and a modified e-Delphi study with methodological experts to identify core criteria. TOOL DESCRIPTION: The MMAT assesses the quality of qualitative, quantitative, and mixed methods studies. It focuses on methodological criteria and includes five core quality criteria for each of the following five categories of study designs: (a) qualitative, (b) randomized controlled, (c) nonrandomized, (d) quantitative descriptive, and (e) mixed methods. CONCLUSION: The MMAT is a unique tool that can be used to appraise the quality of different study designs. Also, by limiting to core criteria, the MMAT can provide a more efficient appraisal.

Three techniques for integrating data in mixed methods studies

Abstract: Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis

The quality of mixed methods studies in health services research.

Abstract: ObjectivesTo assess the quality of mixed methods studies in health services research (HSR).MethodsWe identified 118 mixed methods studies funded by the Department of Health in England between 1994 and 2004, and obtained proposals and/or final reports for 75. We applied a set of quality questions to both the proposal and report of each study, addressing the success of the study, the mixed methods design, the individual qualitative and quantitative components, the integration between methods and the inferences drawn from completed studies.ResultsMost studies were completed successfully. Researchers mainly ignored the mixed methods design and described only the separate components of a study. There was a lack of justification for, and transparency of, the mixed methods design in both proposals and reports, and this had implications for making judgements about the quality of individual components in the context of the design used. There was also a lack of transparency of the individual methods in terms of cle...

A 12-Item Short-Form Health Survey: Construction of Scales and Preliminary Tests of Reliability and Validity

Abstract: Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.

Qualitative data analysis

Abstract: Good qualitative research uses a systematic and rigorous approach that aims to answer questions concerned with what something is like (such as a patient experience), what people think or feel about something that has happened, and it may address why something has happened as it has. Qualitative data often takes the form of words or text and can include images. Qualitative research covers a very broad range of philosophical underpinnings and methodological approaches. Each has its own particular way of approaching all stages of the research process, including analysis, and has its own terms and techniques, but there are some common threads that run across most of these approaches. This Research Made Simple piece will focus on some of these common threads in the analysis of qualitative research. So you have collected all your qualitative data – you may have a pile of interview transcripts, field-notes, documents and …

Decision aids for people facing health treatment or screening decisions

Abstract: Background Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty. Objectives To assess the effects of decision aids for people facing treatment or screening decisions. Search methods For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008). Selection criteria We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions. Data collection and analysis Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were: A) 'choice made' attributes; B) 'decision-making process' attributes. Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model. Main results This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each. Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies). A) Criteria involving 'choice made' attributes: Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13). B) Criteria involving 'decision-making process' attributes: Decision aids compared to usual care interventions resulted in: a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18); b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); and c) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18). Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice. C) Secondary outcomes Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable. The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive. Authors' conclusions There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values. New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.