Raising nursing student awareness of how it feels to have dementia with the use of 360º video
Bibliographic record
Abstract
Objective: This paper aims to look at raising nursing student awareness of how it feels to have dementia through the use of 360-degree video. We were particularly interested in raising awareness amongst nursing students perceptions of the behavioral and psychological symptoms of dementia to promote holistic care provision guided by an empathetic understanding of how it feels to have dementia.Methods: We used a mixed methods approach to investigate nursing students’ awareness of how it feels to have dementia through the use of the 360-degree videos originally developed for a creative new media arts-exhibit at Toronto Rehabilitation Institute for raising dementia awareness. Data were collected quantitatively and qualitatively with thematic and content analyses. A focus group was conducted to explore participants’ awareness of how it feels to have dementia by viewing the 360-degree video, empathy towards people with dementia, and knowledge of dementia.Results: The 360-degree video is an arts-based knowledge-translation strategy that was used to raise nursing students’ awareness on behavioural and psychological symptoms of dementia. While the majority of the participants had some levels of awareness and understanding of dementia prior to the viewing of 360-degree video, they were nonetheless challenged by the content of the 360-degree video that led to new perspectives on dementia.Conclusions: The three themes identified in the thematic analysis of the focus group are understanding dementia from a new perspective, embodiment of dementia experience, and self-reflection and practice changes. These themes reflect the usefulness of 360-degree video as an arts-based knowledge translation strategy in raising dementia awareness in nursing students.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".