Self-Compassion and Resilience at Work: A Practice-Oriented Review
Bibliographic record
Abstract
The Problem A key challenge for human resource development (HRD) scholars and practitioners is to understand the individual competences that can be trained and developed to enhance employee resilience at work. Empirical evidence has been accumulated on the benefits of self-compassion—that is, a set of malleable states that can be developed through HRD interventions—for individual resilience, as well as on the factors that can strengthen self-compassion in organizations. Nonetheless, limited efforts have been devoted to translate this critical scientific knowledge into evidence-based, practical insights that could guide the implementation of effective human resource development practices to develop self-compassion and, thereby, increase resilience in organizations. The Solution This article presents a practice-oriented literature review of the benefits of self-compassion for resilience at work. The relationship between self-compassion and resilience at work, as well as the specific workplace factors that foster growth are outlined for practical implications in the workplace. Facilitators are identified as (1) personal factors (i.e., experience of stress and level of self-awareness), (2) contemplative trainings, and (3) leadership styles and listening styles. This article provides evidence-based practical recommendations for the implementation of these factors and of self-compassion in organizations for increased resilience. The Stakeholders Stakeholders of this practice-oriented review are employees (especially those exposed to highly stressful job conditions) and their leaders, human resource developers in charge of setting the development of training and developmental processes to enhance self-compassion and resilience, and HRD research scholars interested in advancing current literature on self-compassion and resilience at work.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".