Reducing fear of water and aquaphobia through 360 degree video use?
Bibliographic record
Abstract
Drowning is a serious public health problem threat claiming the lives of 372,000 people each year worldwide that can be linked to an individual’s ability to swim. Learning to swim requires limited fear of water. This exploratory study investigated the potential interests of 360° video use for reducing fear and apprehension that underpin aquaphobia. Two students aged 11–12 years old who were non-swimmers with a reluctance to enter the water (i.e., a refusal and/or fear of immersion or to immerse only part of the face or the body in water) participated in qualitative interviews while viewing 360° video of an aquatic environment at progressively deeper levels through a head-mounted display (HMD). Three main findings were identified. First, the use of a 360° video viewed in an HMD led students to live an original corporeal immersive experience, a kind of immersion in the pool but experienced outside the pool. Second, students felt a strong emotional engagement between anxiety and curiosity from exploring the aquatic environment. Third, during the viewing situation, students developed and acquired accurate perceptive cues and knowledge related to the aquatic environment. The implications of these findings highlight the benefits of 360° video use as a tool to enhance greater confidence and familiarity with the aquatic environment to support learning and reduce phobia in non-swimmers. Limitations of the study and future research directions are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".