Swimming against the Tide: A Mixed-Methods Study of how the MARKERS Educator Wellbeing Program Changed Educators’ Relational Space
Bibliographic record
Abstract
Abstract Effective educator wellbeing interventions should consider the individual, relational, and contextual influences on educator wellbeing. Given the gap between the effectiveness of positive psychology interventions (PPIs) and their real-world success, it is essential to understand and adapt to the school context when integrating psychological interventions into educational settings. The MARKERS (Multiple Action Responsive Kit for Educator, Relational, and School wellbeing) educator wellbeing program is multi-level, designed to consider the individual, relational, and contextual influences on wellbeing. Its multi-foci design also allowed for adaptations to specific contexts. This study examines the impact of the MARKERS program in one school in Aotearoa New Zealand. We use a mixed methods case study approach that draws on measures of educator wellbeing, social network measures of energising interactions, and focus group data. The use of stochastic actor-oriented models (SAOMs) allowed us to examine changes to the social network over time. Findings show that MARKERS program participants experienced a significant positive change in their relational space and experienced more energising interactions, but they were ‘swimming against the tide’ as other staff in the school had fewer energising interactions with their colleagues. Our study illustrates the importance of considering the relational and contextual influences on wellbeing when evaluating educator wellbeing interventions.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".