Flaws and all: How mindfulness reduces error hiding by enhancing authentic functioning.
Bibliographic record
Abstract
Hiding errors can undermine safety by amplifying the risks of undetected errors. This article extends research on occupational safety by investigating error hiding in hospitals and applies self-determination theory to examine how mindfulness decreases error hiding through authentic functioning. We examined this research model in a randomized control trial (mindfulness training vs. active control group vs. waitlist control group) within a hospital setting. First, we used latent growth modeling to confirm that our variables were related as hypothesized, both statically or cross-sectionally as well as dynamically as they evolved over time. Next, we analyzed whether changes in these variables were a function of the intervention and confirmed the effects of the mindfulness intervention on authentic functioning and indirectly on error hiding. To elaborate on the role of authentic functioning, in a third step, we qualitatively explored the phenomenological experience of change experienced by participants in mindfulness and Pilates training. Our findings reveal that error hiding is attenuated because mindfulness encourages a receptive view of one's whole self, and authentic functioning enables an open and nondefensive way of relating to positive and negative information about oneself. These results add to research on mindfulness in organizations, error hiding, and occupational safety. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".