How digital wellness is represented in school digital literacy and citizenship models: a qualitative comparative analysis
Bibliographic record
Abstract
Research has begun to examine the role of digital wellness in educational contexts, suggesting that cultivating competencies such as self-regulation, emotional resilience, and intentional technology use may support healthier digital engagement and enhance both academic learning and student well-being. Despite its relevance, digital wellness remains underrepresented in educational discourse, often overshadowed by the dominant emphasis on digital literacy and digital citizenship in schools. Using the Qualitative Comparative Analysis-Based Research Synthesis (QCARS) method, this paper explored the representation of digital wellness within existing digital literacy and digital citizenship educational models or frameworks. In alignment with QCARS methodology, the synthesis was both descriptive and interpretive. Educational frameworks and models from the last 10–15 years (N = 15) were analyzed using a truth table informed by the QCARS method. Results revealed that fourteen of the twenty-four frameworks incorporated digital wellness constructs to varying degrees. The majority referred to digital wellness as a ‘subcomponent’ of digital literacy or digital competence, affirming their status as educational priorities globally. A notable observation, and a potential limitation of this analysis, is the terminological ambiguity, posing challenges for identification and comparison. As schools continue to navigate increasingly digital environments, cultivating not only digital competence but also digital wellness is critical.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".