Beyond stress reduction: A conceptual model of intrapersonal transformation through mindfulness interventions
Bibliographic record
Abstract
Mindfulness-based programs are becoming increasingly common in workplace settings as a means to manage worker stress and enhance resilience.The healthcare sector has been an early-adopter of mindfulness as a means to mitigate workers’ exposure to trauma and high levels of stress, which can result in fatigue, burnout and sub-optimal patient care. In spite of the avalanche of new empirical and theoretical studies of mindfulness programs published over the past ten years, there remains a relative dearth of high-quality qualitative research describing the process and outcomes of programs from the perspective of participants, including their longitudinal impacts. This paper describes qualitative findings of an evaluation of two workplace mindfulness programs involving over 190 healthcare workers, using pre- and post-intervention qualitative surveys as well as focus groups. The study explores participant experiences, described in their own words, and the impact of these programs one year after completion. We draw on the stories gathered from participants to craft an inductive model of mindfulness and its impacts on the lives of novice practitioners. Using metaphor as a method to elucidate this model, we describe the transformative impacts of mindfulness for workers, including impacts on stress, resilience, insight and well-being.We also discuss how qualitative research methods can inform efforts to enhance the quality and evaluate the impact of mindfulness programs in the workplace.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".