A PROPOSED PILOT STUDY OF A GRATITUDE PRACTICE PROGRAM TO INCREASE GRATITUDE AMONG EDUCATORS: THE FIRST STEP TOWARDS EXPLORING THE POTENTIAL OF GRATITUDE PRACTICE TO INCREASE WORK ENGAGEMENT AND BUFFER AGAINST AND DECREASE BURNOUT
Bibliographic record
Abstract
Burnout in educational settings is an international issue. Drawing on practitioner experience in Flexi schools for disenfranchised young people and the methods of positive psychology, this paper outlines a gratitude practice program (GPP) as a proposed intervention to address burnout within Flexi schools. The proposed intervention comprises a full-day workshop and 10 weekly group coaching sessions that aim to develop gratitude practice among teachers. The intention of the paper is to consider the GPP’s capacity to increase gratitude and the potential benefit of the program to participants. This proposed pilot study will be delivered to 14 educators in a single school. A pretest-posttest, multi-method design of evaluation will be presented and discussed. The GPP’s capacity to increase gratitude will be evaluated utilising a gratitude questionnaire. A focus group will be used to ascertain the benefit of the program. It is predicted that the GPP will increase gratitude and will be found beneficial by participants. The potential of the GPP to increase work engagement is discussed in terms of future studies. This proposed pilot study offers a practical intervention that can potentially address the real-world problem of burnout in alternative educational settings.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".