Examining the Efficacy of an Online Program to Cultivate Mindfulness and Self-Compassion Skills (Mind-OP): Randomized Controlled Trial on Amazon’s Mechanical Turk
Bibliographic record
Abstract
Objectives: The demand for effective psychological treatments for depression, anxiety, and heightened stress is far outstripping their supply. Accordingly, internet delivered, self-help interventions offer hope to many people, as they can be easily accessed and at a fraction of the price of face-to-face options. Mindfulness and self-compassion are particularly exciting approaches, as evidence suggests interventions that cultivate these skills are effective in reducing depression, anxiety, and heightened stress. We examined the efficacy of a newly developed program that combines mindfulness and self-compassion exercises into a brief self-guided intervention (Mind-OP). The secondary aim of this study was to investigate the feasibility of conducting a randomized-controlled trial entirely on a popular crowdsourcing platform, Amazon’s Mechanical Turk (MTurk). Methods: We randomized 456 participants reporting heightened depression, anxiety, or stress to one of two conditions: the 4-week Mind-OP intervention (n= 227) or to an active control condition (n =229) where participants watched nature videos superimposed onto relaxing meditation music for four consecutive weeks. We administered measures of anxiety, depression, perceived stress, dispositional and state mindfulness, self-compassion, and nonattachment. Results: Intent-to-treat and per-protocol analyses revealed that, compared to participants in the control condition, participants in the Mind-OP intervention condition reported significantly less anxiety and stress at the end of the trial, as well as significantly greater mindfulness, self-compassion, and nonattachment. Conclusions: Mind-OP appears efficacious in reducing anxiety symptoms and perceived stress among MTurk participants. We highlight issues (e.g., attrition) related to feasibility of conducting randomized trials on crowdsourcing platforms such as MTurk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".