Co-Design and Evaluation of a Gamified E-Resource About Healthcare Decarbonisation: A Study Protocol
Bibliographic record
Abstract
Climate change poses a major global health threat, with healthcare systems contributing substantially to global greenhouse gas emissions. Health professionals and students play an essential role in advancing sustainable practice, yet many lack the knowledge, skills, and confidence needed to address the environmental impacts of healthcare. This study aims to co-design and evaluate a gamified e-resource that enhances pre-registration health profession students' knowledge, self-efficacy, and attitudes towards healthcare decarbonisation, while encouraging sustainable behaviour change. A sequential explanatory design will be employed in three phases: (1) a scoping review of the literature; (2) four co-design workshops with students (n = 20) followed by post-workshop focus groups using focused ethnography to explore co-design experiences; and (3) pre- and post-test questionnaires (n = 200) assessing knowledge, attitudes, self-efficacy, behaviours, willingness to act, and usability, followed by focus groups (n = 30) exploring behavioural changes after using the e-resource. The study will generate evidence on how a co-designed, gamified e-resources influence student learning and engagement with healthcare decarbonisation. Findings will inform the integration of sustainability and decarbonisation principles within education and support efforts to equip future health professionals with the competencies required for a low-carbon healthcare system.
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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 | 0.062 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".