Effectiveness of online mindfulness interventions on medical students’ mental health: a systematic review
Bibliographic record
Abstract
BACKGROUND: Medical school typically presents students with a combination of academic and personal stressors that may lead to substandard mental health wellbeing. Meditation practices such as mindfulness facilitate a greater awareness of one's thoughts and feelings, thereby decreasing emotional reactivity. The use of mindfulness-based interventions delivered online has considerable potential in fostering self-care and helping medical students to handle mental health challenges. We examined the available evidence on the use of online mindfulness interventions in order to determine whether they are feasible and effective for improving medical students' mental health. METHODS: We performed a systematic review guided by PRISMA guidelines and utilised the following databases: ProQuest, Medline, PubMed, PsycINFO, Web of Science, IEEE Explore, Cochrane, and CINAHL. The key search terms used include mindfulness, cognitive behavioural therapy, acceptance and commitment therapy, online, web, virtual, internet cyber, app, medical students, residency students, and residents. English-language articles published in the last ten years that described online interventions for medical students or residents were included in the review. RESULTS: Two studies describing the impact of online mindfulness interventions on medical students' mental health were identified. Research in this domain is nascent; available qualitative and quantitative evidence suggests benefits in self-compassion, perceived stress, cognitive skill use, mindfulness, creating coping mechanisms, and greater awareness of emotions and feelings. There was no evidence of the effectiveness of online mindfulness interventions on depression, anxiety and burnout. There was, however, general low program usage and participation tended to diminish near the conclusion of the interventions. CONCLUSIONS: The evidence found in the systematic review exhibits the potential for online mindfulness interventions to be effective in addressing some mental health challenges of medical students. There was insufficient evidence to support the use of online mindfulness interventions for burnout, depression, and anxiety. Longitudinal studies with randomised controlled trials are required to generate stronger and robust evidence.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".