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How Single Is "Single" - Some Pragmatic Reflections on Single Versus Multifaceted Interventions to Facilitate Implementation: Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multifaceted Implementation Strategies - Is There a Simple Answer to a Complex Question?".

ABSTRACT: An earlier overview of systematic reviews and a subsequent editorial on single-component versus multifaceted interventions to promote knowledge translation (KT) highlight complex issues in implementation science. In this supplemented commentary, further aspects are in focus; we propose examples from (KT) studies probing the issue of single interventions. A main point is that defining what is a single and what is a multifaceted intervention can be ambiguous, depending on how the intervention is conceived. Further, we suggest additional perspectives in terms of strategies to facilitate implementation. More specifically, we argue for a need to depict not only what activities are done in implementation interventions, but to unpack functions in particular contexts, in order to support the progress of implementation science.

SUBMITTER: Eldh AC

PROVIDER: S-EPMC4594114 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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How Single Is "Single" - Some Pragmatic Reflections on Single Versus Multifaceted Interventions to Facilitate Implementation: Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multifaceted Implementation Strategies - Is There a Simple Answer to a Complex Question?".

Eldh Ann Catrine AC Wallin Lars L

International journal of health policy and management 20150718 10

An earlier overview of systematic reviews and a subsequent editorial on single-component versus multifaceted interventions to promote knowledge translation (KT) highlight complex issues in implementation science. In this supplemented commentary, further aspects are in focus; we propose examples from (KT) studies probing the issue of single interventions. A main point is that defining what is a single and what is a multifaceted intervention can be ambiguous, depending on how the intervention is c ...[more]

PMID: 26673184

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Project description:Guidelines are often slowly adapted into clinical practice. However, actively supporting healthcare professionals in evidence-based treatment may speed up guideline implementation. Danish low back pain (LBP) guidelines focus on primary care treatment of LBP, to reduce referrals from primary care to secondary care. The primary aim of this project was to reduce secondary care referral within 12 weeks by a multifaceted implementation strategy (MuIS).In a cluster randomised design, 189 general practices from the North Denmark Region were invited to participate. Practices were randomised (1:1) and stratified by practice size to MuIS (28 practices) or a passive implementation strategy (PaIS; 32 practices). Included were patients with LBP aged 18 to 65 years who were able to complete questionnaires, had no serious underlying pathology, and were not pregnant. We developed a MuIS including outreach visits, quality reports, and the STarT Back Tool for subgrouping patients with LBP. Both groups were offered the usual dissemination of guidelines, guideline-concordant structuring of the medical record, and a new referral opportunity for patients with psycho-social problems. In an intention-to-treat analysis, the primary and secondary outcomes pertained to the patient, and a cost-effectiveness analysis was performed from a healthcare sector perspective. Patients and the assessment of outcomes were blinded. Practices and caregivers delivering the interventions were not blinded.Between January 2013 and July 2014, 60 practices were included, of which 54 practices (28 MuIS, 26 PaIS) included 1101 patients (539 MuIS, 562 PaIS). Follow-up data for the primary outcome were available on 100 % of these patients. Twenty-seven patients (5.0 %) in the MuIS group were referred to secondary care vs. 59 patients (10.5 %) in the PaIS group. The adjusted odds ratio (AOR) was 0.52 [95 % CI 0.30 to 0.90; p?=?0.020]. The MuIS was cost-saving £-93.20 (£406.51 vs. £499.71 per patient) after 12 weeks. Conversely, the MuIS resulted in less satisfied patients after 52 weeks (AOR 0.50 [95 % CI 0.31 to 0.81; p?=?0.004]).Using a MuIS changed general practice referral behaviour and was cost effective, but patients in the MuIS group were less satisfied. This study supports the application of a MuIS when implementing guidelines.ClinicalTrials.gov, NCT01699256.

Project description:Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.

Project description:IntroductionThroughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minorities, were disproportionately impacted by illness, hospitalization, and death. Equity in clinical trials means that the participants in clinical trials represent the people who are most likely to have the health condition and need the treatment that the trial is testing. Infodemiology approaches examining user conversations on social media platforms have the potential to elucidate specific barriers and challenges related to clinical trial participation, including among racial and ethnic minority populations.Materials and methodsThe study retrospectively collected and analyzed user question and answer posts from Quora in October 2021 using an inductive content coding approach. We also examined user's publicly available profile metadata to identify racial and ethnic minority users to capture their experiences, attitudes, topics, and discussions of barriers to COVID-19 vaccine trials.ResultsA total of 1073 questions and 7479 answers were collected. A total of 763 questions and 2548 answers were identified as related to COVID-19 vaccine clinical trials. The majority of these online interactions focused on asking questions and sharing knowledge and opinions about COVID-19 vaccine trials, including major topics related to: (a) interpreting whether clinical trial results could be trusted; (b) questions about vaccine efficacy and safety; (c) understanding trial design, regulatory considerations, and vaccine platforms; and (d) questions about trial enrollment, length, and adequate representation. Additionally, four major barriers discussed included: (i) disagreement from users regarding whether clinical trials require representation from different racial and ethnic minorities; (ii) concerns regarding the safety of trials when participating; (iii) lack of knowledge on how to participate in a trial; and (iv) questions of whether participants could withdraw from a trial to access an approved COVID-19 vaccine.ConclusionsOur study found active user discussions related to COVID-19 vaccine clinical trials on Quora, including those specific to minority health topics and those posted by a smaller group of self-identified racial and ethnic minority online users. Results from this study can help identify barriers to participation among the general public and underrepresented groups while also supporting the design of future outreach strategies to help with recruitment and inclusive trial participation.

Dataset Information

How Single Is "Single" - Some Pragmatic Reflections on Single Versus Multifaceted Interventions to Facilitate Implementation: Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multifaceted Implementation Strategies - Is There a Simple Answer to a Complex Question?".

Publications

How Single Is "Single" - Some Pragmatic Reflections on Single Versus Multifaceted Interventions to Facilitate Implementation: Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multifaceted Implementation Strategies - Is There a Simple Answer to a Complex Question?".

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