The effects of loving-kindness interventions on positive and negative mental health outcomes: A systematic review and meta-analysis
Bibliographic record
Abstract
Loving-kindness meditations involve sending feelings of kindness and care to a series of people including oneself, loved ones, strangers, and all beings. Loving-kindness interventions (LKIs), which include knowledge and/or practice related to loving-kindness, have been gaining attention as a potential intervention for improving mental health in adults. This meta-analysis synthesized the effects of LKIs on both positive (i.e., mindfulness, compassion, positive affect) and negative (i.e., negative affect, psychological symptoms) indices of mental health across comparison types (i.e., passive control, active control, alternative treatment) and general sample types (i.e., community, university), and explored characteristics of LKIs that may impact their effectiveness (i.e., intervention format, intervention length, presence/absence of a live facilitator). Following a systematic review of six databases in November 2023, 23 randomized controlled studies met eligibility criteria and were included in the review. Relative to passive control groups, LKIs had positive effects on mindfulness, compassion, positive affect, negative affect, and psychological symptoms; these effects were non-significant relative to active control groups and alternative therapeutic treatments. Notably, the effects of LKIs did not differ as a function of sample type, intervention format, intervention length, or the presence/absence of a live facilitator. Findings provide support for the effectiveness of LKIs relative to passive control conditions, as well as their potential comparability to alternative evidence-based therapeutic treatments, and provide insight into resource-effective approaches to the delivery of effective LKIs. However, additional studies are needed to confirm the impacts of LKIs relative to other interventions in the field.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".