The Complexity of Assessing Ministry-Specific Satisfaction and Stress
Bibliographic record
Abstract
Christian ministry work can be gratifying, but it also carries a risk of burnout. Despite research documenting clergy stress and wellness, few questionnaires measure ministry life aspects that contribute to clergy burnout or wellness. To fill this gap, we developed the positive aspects (PAI) and negative aspects (NAI) inventories. These questionnaires measure the intensity and frequency of positive and negative aspects of ministry life. The present research further tests the viability of the PAI and NAI. Confirmatory factor analyses suggest that the PAI is best represented by 17 factors, whereas 12 factors best characterize the NAI. Importantly, correlation patterns between PAI and NAI scores and indices of burnout suggest distinct patterns of burnout. Furthermore, exploratory tests of PAI and NAI scores by gender, ordination status, and years in ministry demonstrated crucial differences. This research advances the understanding of clergy wellness and has implications for assessment and intervention.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".