Mindfulness buffers the negative effects of social media overuse on work effort through state self-control during crisis: a daily diary study
Bibliographic record
Abstract
To examine the impact of social media overuse on work effort amidst crisis, we apply Conservation of Resources theory to explain how social media acts as an internal stressor that depletes daily self-regulatory resources, resulting in lower daily work effort. Further, we explore whether daily mindfulness acts as a personal resource that individuals can draw from to buffer the negative effects of social media on organisational outcomes. To examine our theoretical model, we use a type of experience sampling methodology (ESM). We followed 227 participants partaking in a 30-day mindfulness challenge during the height of the COVID-19 pandemic (May/June 2020) through a daily diary study resulting in 3,851 data points at the within-person level. We examined the association between daily social media overuse and employee work effort through the mediating mechanism of daily state self-control capacity and daily mindfulness as a potential moderator of this relationship. Results suggest that daily social media overuse was related to decreased daily work effort through reduced state self-control; however, participants experienced less decline in work effort following social media overuse on days that they were more mindful, suggesting mindfulness acts as a personal resource that buffers the strain on daily state self-control capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".