“God is my doctor”: mindfulness meditation/prayer as a spiritual well-being coping strategy for Jamaican school principals to manage their work-related stress and anxiety
Bibliographic record
Abstract
Purpose This article explores Jamaican secondary school principals' use of mindfulness meditation as a spiritual well-being strategy to manage their work-related stress and anxiety. Design/methodology/approach The author used qualitative semi-structured interviews to collect the data from 12 Jamaican secondary school principals working in schools supporting rural, urban and inner-city school communities. Thematic coding of the analyzed data was used to understand how principals deal with their work-related stress and anxiety. Findings The findings indicate that Jamaican school principals are experiencing work-related stress and anxiety as a result of work intensification, and use mindfulness meditation/prayer as a spiritual coping strategy. The data indicate that principals' primary source of support is their spiritual belief – faith in God and mindfulness meditation/prayer – when dealing with issues related to well-being. Originality/value This article explores the use of mindfulness meditation as a non-secular coping strategy, and focuses on an understudied area of educational administration research: Jamaican school principals' well-being. The findings can help inform future education and health policy around occupational health and well-being for professionals, and lay the foundation for greater studies on principal well-being in Jamaican and the Caribbean more generally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".