Critical engagement with digital health: A socio-material analysis of physical education teachers’ digital health mind maps
Bibliographic record
Abstract
Objective: This paper forms part of a DigihealthPE project in which we have been working with physical education (PE) teachers to co-create critical and embodied digital health pedagogies. As part of the project, we invited PE teachers to mind map their personal engagements with digital health technologies. We aimed to explore the potential openings and opportunities (and limitations) within these maps for critical thinking and action. Method: Data were generated during a workshop with 12 PE teachers in Scotland. Informed by new materialism, we focus on the human and non-human factors and intra-actions evident within six narrative portraits generated from teachers’ mind maps. Results: Our findings suggest that teachers were engaging complexly and extensively with digital health technologies, which we considered an opening for further critical work. Importantly, experiences of strong (negative) affect had the potential to transform engagements with digital health technologies. Conclusion: We conclude by exploring how the process of mind mapping helped us to see further opportunities for supporting teachers to engage critically with digital health technologies. We also argue that new materialist-informed critical practices in education may have transformative potential for helping teachers and pupils to engage critically with the moving body, technology and health.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| 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".