Mindfulness and psychological capital: examining the role of intention from the person perspective in a multi-week mindfulness training program
Bibliographic record
Abstract
Purpose When evaluating the efficacy of mindfulness interventions, most studies take a linear approach to explore how an intervention impacts different outcomes for individuals, and rarely is the role of intention examined. This research takes a configural approach to consider how various elements of a participant’s training expectations and their experience in the training condition combine to predict increases in psychological capital. Design/methodology/approach Employees from hospital settings were randomized into three conditions (mindfulness training, active control (Pilates), and wait-list control group) and completed surveys at three time points (baseline, post-training and three months post-training). A qualitative comparative analysis was applied to see what combinations of motivational elements were associated with increases in psychological capital. Findings We find that all three conditions can boost their psychological capital based on different configurations involving efficacy beliefs, baseline states of well-being (psychological capital and perceived stress) and changes in levels of mindfulness and perceived stress. Research limitations/implications Individual characteristics, like motivation, expectancy and baseline needs, are an important consideration in addition to the training condition itself when determining whether a training is efficacious. Practical implications It is of increasing importance that organizations find ways to support employee well-being. Offering a variety of psychological and physical interventions can improve psychological capital. Applying needs assessments that clarify the desires, needs and expectations employees hold may help with intervention efficacy. Originality/value The current study offers an innovative methodology through which realist evaluation approaches can consider multiple factors to predict outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".