Clergy Resilience: Accessing Supportive Resources to Balance the Impact of Role-Related Stress and Adversity
Bibliographic record
Abstract
Resilience is a helpful construct when considering how to support clergy well-being. The purpose of this study was to gain knowledge about clergy resilience, specifically those resources that clergy perceived had supported their professional resilience. The study gave attention to aspects of preservice training and professional development that helped to foster clergy resilience and initiatives that clergy desired to further support their resilience. Clergy reported multiple resources that supported their resilience including supports for spiritual life, relational supports, personal aspects, and organizational practices. Spiritual dimensions of support for resilience were prominent for clergy, especially the centrality of calling to ministry, theological meaning-making, and relationship with God. Participants also revealed helpful aspects of preservice training and professional development. Aspects of preservice training included rigorous discernment and screening of their callings and the inclusion of required practices, such as spiritual direction or mentorship. Aspects of professional development included a variety of skill development opportunities, lifelong learning, conferences, and networking with peers. Participants reported their desire for initiatives that included more wellness opportunities and an increased organizational prioritization of clergy wellness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".