The Challenge of Accounting for the Moderator Effect of Risk Exposure on the Effectiveness of Mindfulness-Based Treatments for Youth
Bibliographic record
Abstract
Although many positive psychology interventions like mindfulness-based treatments (MBTs) for youth have been used with a wide array of risk exposed populations (children living in poverty, victims of violence, displaced persons, children with disabilities, etc.), the efficacy of MBTs with regard to the level and domain of risk exposure has been largely overlooked. This oversight contributes to a perception of positive psychology as being decontextualized. The purpose of this meta-analysis is to better understand how a young person's level and domain of risk exposure influences the efficacy of MBTs. Published studies were included if participants were children or adolescents, were RCTs or cluster RCTs, and focused on the effect of an MBT or an MBT component. Specifically, 58 studies were qualitatively analyzed and 42 were quantitatively analyzed. The qualitative analysis evidenced a lack of objectivity in the assessment of risk exposure, with many papers providing very little or no detail on participants' level or domain of risk exposure (whether threats to psychological wellbeing originate with the individual's biology or cognitions, or the social, built or natural environment). Our quantitative analysis showed that MBTs significantly improved main outcomes relative to the control conditions, with a small-to-moderate effect size. Where it was possible to identify the level of risk exposure, we found that MBTs positively moderated treatment response, with better response among those participants with a higher level of risk. We conclude that future Positive Psychology research will need to better account for risk exposure if the discipline is to improve our understanding of which populations benefit most from interventions that promote mindfulness. Supplementary Information: The online version contains supplementary material available at 10.1007/s41042-023-00145-y.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.222 | 0.405 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".