While an extensive array of existing evidence-based practices (EBPs) have the potential to improve patient outcomes, little is known about how to implement EBPs on a larger scale. Therefore, we sought to identify effective strategies for scaling up EBPs in primary care. We conducted a systematic review with the following inclusion criteria: (i) study design: randomized and non-randomized controlled trials, before-and-after (with/without control), and interrupted time series; (ii) participants: primary care-related units (e.g., clinical sites, patients); (iii) intervention: any strategy used to scale up an EBP; (iv) comparator: no restrictions; and (v) outcomes: no restrictions. We searched MEDLINE, Embase, PsycINFO, Web of Science, CINAHL, and the Cochrane Library from database inception to August 2016 and consulted clinical trial registries and gray literature. Two reviewers independently selected eligible studies, then extracted and analyzed data following the Cochrane methodology. We extracted components of scaling-up strategies and classified them into five categories: infrastructure, policy/regulation, financial, human resources-related, and patient involvement. We extracted scaling-up process outcomes, such as coverage, and provider/patient outcomes. We validated data extraction with study authors. We included 14 studies. They were published since 2003 and primarily conducted in low-/middle-income countries (n = 11). Most were funded by governmental organizations (n = 8). The clinical area most represented was infectious diseases (HIV, tuberculosis, and malaria, n = 8), followed by newborn/child care (n = 4), depression (n = 1), and preventing seniors’ falls (n = 1). Study designs were mostly before-and-after (without control, n = 8). The most frequently targeted unit of scaling up was the clinical site (n = 11). The component of a scaling-up strategy most frequently mentioned was human resource-related (n = 12). All studies reported patient/provider outcomes. Three studies reported scaling-up coverage, but no study quantitatively reported achieving a coverage of 80% in combination with a favorable impact. We found few studies assessing strategies for scaling up EBPs in primary care settings. It is uncertain whether any strategies were effective as most studies focused more on patient/provider outcomes and less on scaling-up process outcomes. Minimal consensus on the metrics of scaling up are needed for assessing the scaling up of EBPs in primary care. This review is registered as PROSPERO CRD42016041461 .

Background: The last decade has seen growing interest in scaling up of innovations to strengthen healthcare systems. However, the lack of appropriate methods for determining their potential for scale-up is an unfortunate global handicap. Thus, we aimed to review tools proposed for assessing the scalability of innovations in health.Methods: We conducted a systematic review following the COSMIN methodology. We included any empirical research which aimed to investigate the creation, validation or interpretability of a scalability assessment tool in health. We searched Embase, MEDLINE, CINAHL, Web of Science, PsycINFO, Cochrane Library and ERIC from their inception to 20 March 2019. We also searched relevant websites, screened the reference lists of relevant reports and consulted experts in the field. Two reviewers independently selected and extracted eligible reports and assessed the methodological quality of tools. We summarized data using a narrative approach involving thematic syntheses and descriptive statistics.Results: We identified 31 reports describing 21 tools. Types of tools included criteria (47.6%), scales (33.3%) and checklists (19.0%). Most tools were published from 2010 onwards (90.5%), in open-access sources (85.7%) and funded by governmental or nongovernmental organizations (76.2%). All tools were in English; four were translated into French or Spanish (19.0%). Tool creation involved single (23.8%) or multiple (19.0%) types of stakeholders, or stakeholder involvement was not reported (57.1%). No studies reported involving patients or the public, or reported the sex of tool creators. Tools were created for use in high-income countries (28.6%), low- or middle-income countries (19.0%), or both (9.5%), or for transferring innovations from low- or middle-income countries to high-income countries (4.8%). Healthcare levels included public or population health (47.6%), primary healthcare (33.3%) and home care (4.8%). Most tools provided limited information on content validity (85.7%), and none reported on other measurement properties. The methodological quality of tools was deemed inadequate (61.9%) or doubtful (38.1%).Conclusions: We inventoried tools for assessing the scalability of innovations in health. Existing tools are as yet of limited utility for assessing scalability in health. More work needs to be done to establish key psychometric properties of these tools.Trial registrationWe registered this review with PROSPERO (identifier: CRD42019107095)

Little is known about cancer survivors’ sexual health (SH)—particularly, from well after diagnosis and in cancers unrelated to sexuality. This study aimed to assess SH deterioration five years after diagnosis. We analyzed data from the French national VIe après le CANcer (VICAN) survey. Six items from the Relationship and Sexuality Scale were used to assess SH. Respondents were grouped according to an ascending hierarchical classification in four clusters: strong, moderate, and weak deterioration or stable (WD, SD, MD, or St). Out of 2195 eligible participants, 57.3% reported substantial SH deterioration as either SD (30.8%) or MD (26.5%), while WD and St accounted for 31.2% and 11.5% of respondents, respectively. Substantial deterioration was reported in all cancer sites (from 27.7% in melanoma to 83.1% in prostate). Treatment type, cancer sequelae, and pain, as well as psychological consequences (depression and anxiety, especially for younger patients) were associated with substantial SH deterioration. The same factors were identified after restricting the analysis to survivors of cancers unrelated to sexuality. Five years after diagnosis, the majority of cancer survivors reported SH deterioration. Interventions should be developed to improve SH regardless of cancer site. Particular attention should be paid to depression and anxiety, especially in younger survivors.

Background. Older adults receiving home care services often face decisions related to aging, illness, and loss of autonomy. To inform tailored shared decision making interventions, we assessed their decisional needs by asking about the most common difficult decisions, measured associated decisional conflict, and identified factors associated with it. Methods. In March 2020, we conducted a cross-sectional survey with a pan-Canadian Web-based panel of older adults (≥65 y) receiving home care services. For a difficult decision they had faced in the past year, we evaluated clinically significant decisional conflict (CSDC) using the 16-item Decisional Conflict Scale (score 0–100) with a >37.5 cutoff. To identify factors associated with CSDC, we performed descriptive, bivariable, and multivariable analyses using the stepwise selection method with an assumed entry and exit significance level of 0.15 and 0.20, respectively. Final model selection was based on the Bayesian information criterion. Results. Among 460 participants with an average age of 72.5 y, difficult decisions were, in order of frequency, about housing and safety (57.2%), managing health conditions (21.8%), and end-of-life care (8.3%). CSDC was experienced by 14.6% (95% confidence interval [CI]: 11.5%, 18.1%) of respondents on all decision points. Factors associated with CSDC included household size = 1 (OR [95% CI]: 1.81 [0.99, 3.33]; P = 0.27), household size = 3 (2.66 [0.78, 8.98]; P = 0.83), and household size = 4 (6.91 [2.23, 21.39]; P = 0.014); preferred option not matching the decision made (4.05 [2.05, 7.97]; P < 0.001); passive role in decision making (5.13 [1.78, 14.77]; P = 0.002); and lower quality of life (0.70 [0.57, 0.87]; P<0.001). Discussion. Some older adults receiving home care services in Canada experience CSDC when facing difficult decisions. Shared decision-making interventions could mitigate associated factors. Highlights: This is the first study in Canada to assess the decisional needs of older adults receiving care at home and to identify their most common difficult decisions. Difficult decisions most frequently made were about housing and safety. The most significant decisional conflict was experienced by people making decisions about palliative care. When their quality-of-life score was low, older adults experienced clinically significant decision conflict.

Background. Informal caregivers are regularly faced with difficult housing decisions for older adults with cognitive impairment. They often regret the decision they made. We aimed to identify factors associated with decision regret among informal caregivers engaging in housing decisions for cognitively impaired older adults. Methods. We performed a secondary analysis of cross-sectional data collected from a cluster-randomized trial. Eligible participants were informal caregivers involved in making housing decisions for cognitively impaired older adults. Decision regret was assessed after caregivers’ enrollment in the study using the Decision Regret Scale (DRS), scored from 0 to 100. We used a conceptual framework of potential predictors of regret to identify independent variables. We performed multilevel analyses using a mixed linear model by estimating fixed effects (β) and 95% confidence intervals (CIs). Results. The mean (SD) DRS score of 296 informal caregivers (mean [SD] age, 62 [12] years) was 12.4 (18.4). Factors associated with less decision regret were having a college degree compared to primary education (β [95% CI]: –11.14 [–18.36, –3.92]), being married compared to being single (–5.60 [–10.05, –1.15]), informal caregivers’ perception that a joint process occurred (–0.14 [–0.25, –0.02]), and older adults’ not having a specific housing preference compared to preferring to stay at home (–4.13 [–7.40, –0.86]). Factors associated with more decision regret were being retired compared to being a homemaker (7.74 [1.32, 14.16]), higher burden of care (0.14 [0.05, 0.22]), and higher decisional conflict (0.51 [0.34, 0.67]). Limitations. Our analysis may not illustrate all predictors of decision regret among informal caregivers. Conclusions. Our findings will allow risk-mitigation strategies for informal caregivers at risk of experiencing regret.

Informal caregivers often serve as decision makers for dependent or vulnerable individuals facing health care decisions. Decision regret is one of the most prevalent outcomes reported by informal caregivers who have made such decisions. To examine levels of decision regret and its predictors among informal caregivers who have made health-related decisions for a loved one. We performed a systematic search of Embase, MEDLINE, Web of Science, and Google Scholar up to November 2018. Participants were informal caregivers, and the outcome was decision regret as measured using the Decision Regret Scale (DRS). Two reviewers independently selected eligible studies, extracted data, and assessed the methodological quality of studies using the Mixed Methods Appraisal Tool. We performed a narrative synthesis and presented predictors of decision regret using a conceptual framework, dividing the predictors into decision antecedents, decision-making process, and decision outcomes. We included 16 of 3003 studies identified. Most studies (n = 13) reported a mean DRS score ranging from 7.0 to 32.3 out of 100 (median = 14.3). The methodological quality of studies was acceptable. We organized predictors and their estimated effects (β) or odds ratio (OR) with 95% confidence interval (CI) as follows: decision antecedents (e.g., caregivers’ desire to avoid the decision, OR 2.07, 95% CI [1.04–4.12], P = 0.04), decision-making process (e.g., caregivers’ perception of effective decision making, β = 0.49 [0.05, 0.93], P < 0.01), and decision outcomes (e.g., incontinence, OR = 4.4 [1.1, 18.1], P < 0.001). This review shows that informal caregivers’ level of decision regret is generally low but is high for some decisions. We also identified predictors of regret during different stages of the decision-making process. These findings may guide future research on improving caregivers’ experiences.