Dr. Papola conducted a systematic review of randomized controlled trials (RCTs) evaluating the efficacy of task-shared psychosocial interventions using the PICO framework as follows: P (population) = adults diagnosed with common mental disorders (CMD) or experiencing subthreshold psychological distress, seeking first-level/primary care or identified within the community; I (intervention) = any psychosocial intervention provided by non-specialist providers (NSPs), including mentoring programs, cognitive-behavioral therapy and its derivatives, interpersonal therapy, psychoeducation, problem-solving therapy, and others; C (comparison) = no treatment, treatment as usual, waiting list, or another active psychosocial intervention; O (main outcome) = reduction in symptomatology; S (setting) = any context. The systematic literature search, conducted in line with the above PICO criteria, yielded 13,320 references from four bibliometric databases (PubMed, Embase, PsycINFO, CENTRAL). After a rigorous screening process and full-text review, 34 studies were deemed eligible for inclusion. This phase adhered to Cochrane standards to ensure the reliability and quality of the process.
Dr. Papola then contacted the authors of the 34 selected RCTs to obtain (a) the manuals of the psychosocial interventions used in the experimental arms of their trials so that he could qualitatively analyze the manual and create the taxonomy of components (WP1, WP2), and (b) the individual participant data (IPD) from the trials, with the aim of tailoring the results based on participant characteristics (WP3). To analyze the intervention manuals and extract active treatment-specific components from their text, Dr. Papola and his research collaborators conducted multiple rounds of coding, both collaboratively and independently. Dr. Papola and collaborators created an initial list of eligible elements and reviewed it for duplication and redundancy. Each element was then operationalized and assessed by the research team. This process included several pilot testing rounds, during which four researchers independently coded two or more eligible trials to test and refine the existing taxonomy. Interrater reliability (IRR) was assessed using intraclass correlation (ICC). After each round of coding, the team discussed similarities and discrepancies among the coders and adjusted the taxonomy as needed. By the final round, the coding showed a high level of agreement among the researchers (ICC = 0.85–0.96). The outcome of this process was a three-tiered, tree-shaped taxonomy of treatment-specific components. Notably, although other taxonomies of psychological intervention components have been developed previously, this is the first time such an instrument has been designed to support both qualitative and quantitative analyses. To enhance its utility for quantitative purposes, the taxonomy was structured to include three levels of depth and specificity: the 'family' level, the 'component' level, and the 'technique' level. This hierarchical structure allows the level of component analysis to be tailored to the available data. For instance, more granular analyses at the 'technique' level can be conducted when large datasets are available, while broader but still informative analyses can be performed at the 'family' level when data is more limited. An online survey has been conducted with the authors of the trials to assess their agreement with the classification of the intervention elements.
Parallel to this, thanks to the collaborative spirit of the trial authors Dr. Papola engaged with, he successfully gathered individual participant data (IPDs) from 30 of the 34 trials in the systematic review. This achievement represents the collection of 10,618 IPDs out of a possible 11,969, covering 89% of the total available data. This compilation forms the most extensive IPD database assembled to date in the field of global mental health. We fitted a cNMA additive model integrating both IPDs and AD (depending on data availability) to evaluate the efficacy of each component at the individual level. We implemented the IPDcNMA approach within a hierarchical Bayesian framework, which enables us to estimate main component effects and moderation by individual participant characteristics while accounting for heterogeneity at both the study and participant levels.
