Can Mindfulness Help People Implement a Growth Mindset? Two Field Experiments in Hungary
Bibliographic record
Abstract
Does holding a growth mindset prevent people from experiencing potentially negative or maladaptive thoughts and feelings following academic setbacks? Not necessarily: In our Hungarian sample, a culture high in negative affect, three-fourths of people who endorsed a growth mindset at a maximum level nonetheless reported at least sometimes being judgmental of themselves and ruminating about setbacks. Thus, in two field experiments performed in Hungary, we incorporated mindfulness elements into an existing growth-mindset intervention (Yeager et al., 2019). These elements focused on accepting but distancing the self from negative thoughts and feelings in response to setbacks. This enhanced growth-mindset treatment significantly raised semester grades among university (N=251, Study 1, d=0.29) and reported grades among high school students (N=3,095, Study 2, d=0.17). A growth-mindset intervention alone also raised GPA with an effect size similar to the effect sizes found in prior research (Study 2); however, this effect did not consistently reach significance across models with the present sample size. Although just a first step, the present studies suggest the value of incorporating mindfulness elements to help people manage a tendency toward negative and counterproductive reactions that may remain even following a mindset intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".