Film as a pedagogical tool for climate change and mental health nursing education
Bibliographic record
Abstract
The relationship between climate change and worsening mental health is of increasing concern globally. Climate change is ubiquitous, yet marginalized populations bear a disproportionate burden of the physical and mental health impacts, while youth are more likely to report mental health concerns related to climate change than older generations. Mental health nurses will inevitably see these impacts play out in their practice, thus it is important to explore innovative tools for teaching about and responding to the emotional and psychological impacts of climate change. This perspective paper presents an educational project that utilized film and structured reflection to engage with the intersecting topics of planetary and mental health. The authors created a documentary film that presents the relationship between mental health and climate change as well as an accompanying reflective toolkit. Both the film and toolkit were integrated into an undergraduate course about the health impacts of climate change. This paper explores the relevance of climate change to mental health nursing education and practice, describes the process of creating and integrating the film and toolkit into a course and advances the position that film is an innovative way to engage individuals and communities (such as student or community groups) with the emotional and psychological concerns that arise in response to complex challenges of climate change. More research is needed to better understand the mental health impacts of climate change and to explore novel approaches to education and advocacy about this topic. We hope that sharing our project and experiences will inspire additional discussion and research related to these emerging issues that are of great relevance to mental health nursing.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".