Amanda S. Newton

Background: Treating depression and anxiety in adolescents can be challenging due to interindividual variability in medication response. With current trial-and-error prescribing practices, adolescents may undergo multiple medication changes over months or years before an effective and tolerated drug and dose are identified. Pharmacogenomic (PGx) testing can identify interindividual differences in drug metabolism, and evidence supporting PGx-guided prescribing in adults with mental disorders is growing. However, its impact on pediatric psychotropic prescribing remains underexplored. Methods: This is a protocol for a parallel-arm, multicentre, randomized controlled trial. Canadian adolescents aged 12–17 years who are initiating or switching a selective serotonin reuptake inhibitor (SSRI) for depression and/or an anxiety disorder under physician care are eligible. A total of 452 participants will be randomized 1:1 to PGx-guided SSRI prescribing (experimental) or SSRI prescribing based on current practice guidelines (control). Participants, caregivers, prescribing clinicians, outcome assessors, and investigators will be blinded to treatment allocation. Dual primary outcomes are symptom remission at 12 weeks, measured with the Quick Inventory of Depressive Symptomatology–Adolescent (QIDS-A17-SR) and the Screen for Child Anxiety Related Disorders (SCARED). Secondary outcomes, assessed at 4, 8, and 12 weeks, include participant- and physician-rated changes in depressive and anxiety symptoms, role functioning, health-related quality of life, health care utilization, cost-effectiveness, side-effect burden, medication burden, and adherence. Multiple testing will be addressed using the Hochberg method, and a parallel gated analysis will account for non-actionable genotypes. Secondary analysis will estimate minimal clinically important differences for symptom and role-functioning change with PGx-guided therapy. Discussion: At the time of writing, 36 participants have consented and been randomized to an intervention. This trial will evaluate whether PGx-guided prescribing improves symptom remission in adolescents treated with SSRIs. If efficacious, results should be interpreted with existing pediatric pharmacokinetic, observational, and adult trial data to inform PGx use in managing pediatric anxiety and depressive disorders.

Emerging evidence suggests that the COVID-19 pandemic and associated public health measures, including lockdowns and school closures, have been negatively affecting school-aged children’s psychological wellbeing. To identify supports required to mitigate the negative impacts of the COVID-19 pandemic, we gathered in-depth information on school-aged children’s and parents’ lived experiences of COVID-19 and perceptions of its impact on psychological wellbeing in grade 4–6 students in Canada. In this qualitative study, we conducted telephone-based semi-structured interviews with parents (n = 15) and their children (n = 16) from six schools in small and mid-sized northern prairie communities in Canada. Interviews were analyzed through thematic analysis. Three interrelated themes have emerged. First, the start of COVID-19 brought sudden and stressful changes to children’s lives. Second, disruptions to daily life led to feelings of boredom and lack of purpose. Third, limited opportunities for social interaction led to loneliness and an increase in screen time to seek social connection with peers. Results underscore the need for resilience building and the promotion of positive coping strategies to help school-aged children thrive in the event of future health crises or natural disasters.

Background: We explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to (a) eliminate irrelevant records (automated simulation) and (b) complement the work of a single reviewer (semi-automated simulation). We evaluated user experiences for each tool.Methods: We subjected three SRs to two retrospective screening simulations. In each tool (Abstrackr, DistillerSR, RobotAnalyst), we screened a 200-record training set and downloaded the predicted relevance of the remaining records. We calculated the proportion missed and workload and time savings compared to dual independent screening. To test user experiences, eight research staff tried each tool and completed a survey.Results: Using Abstrackr, DistillerSR, and RobotAnalyst, respectively, the median (range) proportion missed was 5 (0 to 28) percent, 97 (96 to 100) percent, and 70 (23 to 100) percent for the automated simulation and 1 (0 to 2) percent, 2 (0 to 7) percent, and 2 (0 to 4) percent for the semi-automated simulation. The median (range) workload savings was 90 (82 to 93) percent, 99 (98 to 99) percent, and 85 (85 to 88) percent for the automated simulation and 40 (32 to 43) percent, 49 (48 to 49) percent, and 35 (34 to 38) percent for the semi-automated simulation. The median (range) time savings was 154 (91 to 183), 185 (95 to 201), and 157 (86 to 172) hours for the automated simulation and 61 (42 to 82), 92 (46 to 100), and 64 (37 to 71) hours for the semi-automated simulation. Abstrackr identified 33–90% of records missed by a single reviewer. RobotAnalyst performed less well and DistillerSR provided no relative advantage. User experiences depended on user friendliness, qualities of the user interface, features and functions, trustworthiness, ease and speed of obtaining predictions, and practicality of the export file(s).Conclusions: The workload savings afforded in the automated simulation came with increased risk of missing relevant records. Supplementing a single reviewer’s decisions with relevance predictions (semi-automated simulation) sometimes reduced the proportion missed, but performance varied by tool and SR. Designing tools based on reviewers’ self-identified preferences may improve their compatibility with present workflows.Systematic review registration: Not applicable.

Overviews of reviews of healthcare interventions (overviews) integrate information from multiple systematic reviews (SRs) to provide a single synthesis of relevant evidence for decision-making. Overviews may identify multiple SRs that examine the same intervention for the same condition and include some, but not all, of the same primary studies. Different researchers use different approaches to manage these “overlapping SRs,” but each approach has advantages and disadvantages. This study aimed to develop an evidence-based decision tool to help researchers make informed inclusion decisions when conducting overviews of healthcare interventions. We used a two-stage process to develop the decision tool. First, we conducted a multiple case study to obtain empirical evidence upon which the tool is based. We systematically conducted seven overviews five times each, making five different decisions about which SRs to include in the overviews, for a total of 35 overviews; we then examined the impact of the five inclusion decisions on the overviews’ comprehensiveness and challenges, within and across the seven overview cases. Second, we used a structured, iterative process to transform the evidence obtained from the multiple case study into an empirically based decision tool with accompanying descriptive text. The resulting decision tool contains four questions: (1) Do Cochrane SRs likely examine all relevant intervention comparisons and available data? (2) Do the Cochrane SRs overlap? (3) Do the non-Cochrane SRs overlap? (4) Are researchers prepared and able to avoid double-counting outcome data from overlapping SRs, by ensuring that each primary study’s outcome data are extracted from overlapping SRs only once? Guidance is provided to help researchers answer each question, and empirical evidence is provided regarding the advantages, disadvantages, and potential trade-offs of the different inclusion decisions. This evidence-based decision tool is designed to provide researchers with the knowledge and means to make informed inclusion decisions in overviews. The tool can provide practical guidance and support for overview authors by helping them consider questions that could affect the comprehensiveness and complexity of their overviews. We hope this tool will be a useful resource for researchers conducting overviews, and we welcome discussion, testing, and refinement of the proposed tool.

Overviews of reviews (overviews) compile information from multiple systematic reviews (SRs) to provide a single synthesis of relevant evidence for decision-making. Overviews may identify multiple SRs that examine the same intervention for the same condition and include some, but not all, of the same primary studies. There is currently limited guidance on whether and how to include these overlapping SRs in overviews. Our objectives were to assess how different inclusion decisions in overviews of healthcare interventions affect their comprehensiveness and results, and document challenges encountered when making different inclusion decisions in overviews. We used five inclusion decisions to conduct overviews across seven topic areas, resulting in 35 overviews. The inclusion decisions were (1) include all Cochrane and non-Cochrane SRs, (2) include only Cochrane SRs, or consider all Cochrane and non-Cochrane SRs but include only non-overlapping SRs, and in the case of overlapping SRs, select (3) the Cochrane SR, (4) the most recent SR (by publication or search date), or (5) the highest quality SR (assessed using AMSTAR). For each topic area and inclusion scenario, we documented the amount of outcome data lost and changed and the challenges involved. When conducting overviews, including only Cochrane SRs, instead of all SRs, often led to loss/change of outcome data (median 31% of outcomes lost/changed; range 0–100%). Considering all Cochrane and non-Cochrane SRs but including only non-overlapping SRs and selecting the Cochrane SR for groups of overlapping SRs (instead of the most recent or highest quality SRs) allowed the most outcome data to be recaptured (median 42% of lost/changed outcome recaptured; range 28–86%). Across all inclusion scenarios, challenges were encountered when extracting data from overlapping SRs. Overlapping SRs present a methodological challenge for overview authors. This study demonstrates that different inclusion decisions affect the comprehensiveness and results of overviews in different ways, depending in part on whether Cochrane SRs examine all intervention comparisons relevant to the overview. Study results were used to develop an evidence-based decision tool that provides practical guidance for overview authors.

As implementation science advances, the number of interventions to promote the translation of evidence into healthcare, health systems, or health policy is growing. Accordingly, classification schemes for these knowledge translation (KT) interventions have emerged. A recent scoping review identified 51 classification schemes of KT interventions to integrate evidence into healthcare practice; however, the review did not evaluate the quality of the classification schemes or provide detailed information to assist researchers in selecting a scheme for their context and purpose. This study aimed to further examine and assess the quality of these classification schemes of KT interventions, and provide information to aid researchers when selecting a classification scheme. We abstracted the following information from each of the original 51 classification scheme articles: authors’ objectives; purpose of the scheme and field of application; socioecologic level (individual, organizational, community, system); adaptability (broad versus specific); target group (patients, providers, policy-makers), intent (policy, education, practice), and purpose (dissemination versus implementation). Two reviewers independently evaluated the methodological quality of the development of each classification scheme using an adapted version of the AGREE II tool. Based on these assessments, two independent reviewers reached consensus about whether to recommend each scheme for researcher use, or not. Of the 51 original classification schemes, we excluded seven that were not specific classification schemes, not accessible or duplicates. Of the remaining 44 classification schemes, nine were not recommended. Of the 35 recommended classification schemes, ten focused on behaviour change and six focused on population health. Many schemes (n = 29) addressed practice considerations. Fewer schemes addressed educational or policy objectives. Twenty-five classification schemes had broad applicability, six were specific, and four had elements of both. Twenty-three schemes targeted health providers, nine targeted both patients and providers and one targeted policy-makers. Most classification schemes were intended for implementation rather than dissemination. Thirty-five classification schemes of KT interventions were developed and reported with sufficient rigour to be recommended for use by researchers interested in KT in healthcare. Our additional categorization and quality analysis will aid in selecting suitable classification schemes for research initiatives in the field of implementation science.

Researchers, healthcare planners, and policymakers convey a sense of urgency in using eMental healthcare technologies to improve pediatric mental healthcare availability and access. Yet, different stakeholders may focus on different aspects of implementation. We conducted a systematic review to identify implementation foci in research studies and government/organizational documents for eMental healthcare technologies for pediatric mental healthcare. A search of eleven electronic databases and grey literature was conducted. We included research studies and documents from organization and government websites if the focus included eMental healthcare technology for children/adolescents (0–18 years), and implementation was studied and reported (research studies) or goals/recommendations regarding implementation were made (documents). We assessed study quality using the Mixed Methods Appraisal Tool and document quality using the Appraisal of Guidelines for Research & Evaluation II. Implementation information was grouped according to Proctor and colleagues’ implementation outcomes—acceptability, adoption, appropriateness, cost, feasibility, fidelity, penetration, and sustainability—and grouped separately for studies and documents. Twenty research studies and nine government/organizational documents met eligibility criteria. These articles represented implementation of eMental healthcare technologies in the USA (14 studies), United Kingdom (2 documents, 3 studies), Canada (2 documents, 1 study), Australia (4 documents, 1 study), New Zealand (1 study), and the Netherlands (1 document). The quality of research studies was excellent (n = 11), good (n = 6), and poor (n = 1). These eMental health studies focused on the acceptability (70%, n = 14) and appropriateness (50%, n = 10) of eMental healthcare technologies to users and mental healthcare professionals. The quality of government and organizational documents was high (n = 2), medium (n = 6), and low (n = 1). These documents focused on cost (100%, n = 9), penetration (89%, n = 8), feasibility (78%, n = 7), and sustainability (67%, n = 6) of implementing eMental healthcare technology. To date, research studies have largely focused on acceptability and appropriateness, while government/organizational documents state goals and recommendations regarding costs, feasibility, and sustainability of eMental healthcare technologies. These differences suggest that the research evidence available for pediatric eMental healthcare technologies does not reflect the focus of governments and organizations. Partnerships between researchers, healthcare planners, and policymakers may help to align implementation research with policy development, decision-making, and funding foci.

The use of technology such as computers, tablets, and smartphones to improve access to and the delivery of mental health care (eMental Health care) is growing worldwide. However, despite the rapidly expanding evidence base demonstrating the efficacy of eMental Health care, its implementation in clinical practice and health care systems remains fragmented. To date, no peer-reviewed, key-informant studies have reported on the perspectives of decision-makers concerned with whether and how to implement eMental Health care. From September to November 2015, we conducted 31 interviews with key informants responsible for leadership, policy, research, and/ or information technology in organizations influential in the adoption of technology for eMental Health care. Deductive and inductive thematic analyses of transcripts were conducted using the Behavior Change Wheel as an organizing framework. Frequency and intensity effect sizes were calculated for emerging themes to further explore patterns within the data. Key informant responses (n = 31) representing 6 developed countries and multiple organizations showed consensus on common factors impacting implementation: individual and organizational capacities (e.g., computer literacy skills [patients and providers], knowledge gaps about cyber security, limited knowledge of available services); motivational drivers of technology-based care (e.g., extending care, data analytics); and opportunities for health systems to advance eMental Health care implementation (e.g., intersectoral research, rapid testing cycles, sustainable funding). Frequency effect sizes showed strong associations between implementation and credibility, knowledge, workflow, patient empowerment, electronic medical record (EMR) integration, sustained funding and intersectoral networks. Intensity effect sizes showed the highest concentration of statements (>10% of all comments) related to funding, credibility, knowledge gaps, and patient empowerment. This study provides previously unavailable information about key informant perspectives on eMental Health care implementation. The themes that emerged, namely the need to intensify intersectoral research, measure/monitor readiness to implement, define cost-utility benchmarks, raise awareness about available technologies, and test assumptions that ‘proven’ technologies will be easily integrated can inform the design and evaluation of eMental Health care implementation models.