A Contextual Approach to Designing, Implementing, and Adapting a Wellbeing Program: A case Study of the MARKERS Wellbeing Program for Educators
Bibliographic record
Abstract
Abstract Improving educator wellbeing presents a complex challenge due to the variety of influences on wellbeing at the individual, relational, and contextual (school and policy) levels. This complexity contributes to the gap between the effectiveness of positive psychology interventions (PPIs) and their real-world success. To bridge this gap, it is essential to understand and adapt to school context when integrating psychological interventions into educational settings. This study addresses this gap through an approach to developing an educator wellbeing program that embraces the idea of understanding and adapting to context. We use a theory of change approach to designing a program, that outlines the context, design rationale, activities, and outcomes of the program. The program is multi-level, targeting influences on educator wellbeing at the individual, relational, and contextual (school) levels. Additionally, it employs a multi-foci strategy, encompassing a range of activities that target different levels and allow for adaptation. We present a case study of program implementation in one school, exploring contextual factors, adapting the program accordingly, and evaluating the degree to which theory of change outcomes were achieved. Program participants experienced improved wellbeing and a relational space characterised by more positive interactions with colleagues. This design and case study contribute to the ongoing discourse on progressing towards context-specific, whole-school approaches to wellbeing.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".