Determining the Mental Health Climate of the Ministers of Christian Churches in Metro Vancouver After the Reopening of the Churches Since COVID-19 Closure
Bibliographic record
Abstract
The burnout of ministers is real. Ministers are human, just like everyone else in the world. Too many people (such as church officials and leaders) underestimate the adverse effects of burned-out ministers in their churches. The pandemic had a huge impact on all the churches in Metro Vancouver, Canada. Ministers experienced exceptionally high stress and anxiety after the reopening of churches after the COVID-19 closure. Thus, the purpose of this DMIN action research project is to use the Maslach Burnout Inventory (MBI), surveys, questionnaires, and interviews to research, qualify, and quantify the mental health climate of ministers in Metro Vancouver. The mental health climates of church ministers from Vancouver and Burnaby were determined by using the MBI, a demographic survey, a ministerial and lifestyle survey, a questionnaire, as well as significant interview questions. The results reveal that the church ministers of Vancouver and Burnaby both regularly experience low to moderate levels of burnout. Although ministers in Vancouver experienced a high level of burnout, ministers in Burnaby experienced only moderate burnout when analyzed, geographically. This research further proved that the predictors of burnout of ministers were influenced by (1) their country of origin, (2) their number of children and dependents, (3) their ministry locations, (4) their mental health history, and (5) whether they have a self-care plan or not. This research is important as it presents a crucial triage to determine the mental health climate of Christian ministers at any moment in Metro Vancouver.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".