The Effects of Nature Images on Pain in a Simulated Hospital Patient Room
Bibliographic record
Abstract
BACKGROUND: Views of nature have been reported to relieve stress and pain, making nature an ideal medium for use in healthcare settings. In hospitals whose design does not allow for a view of nature, virtual and surrogate views of nature may be viable therapeutic options. OBJECTIVE: This study tests the effects of specific nature images, as defined by Appleton's prospect refuge theory of landscape preference, on participants experiencing pain. The hypotheses were: (1) Nature views are variable in their impact on specific psychological and physiological health status indicators; and (2) Prospect and refuge nature scenes are more therapeutic than hazard nature scenes. The research question was (1) Which nature image categories are most therapeutic as evidenced by reduced pain and positive mood? METHODS: An experiment using mixed methods assessed the effects of four different nature scenes on physiological (blood pressure, heart rate) and psychological (mood) responses when a person was subjected to a pain stressor. Four groups were subjected to a specific nature image category of prospect, refuge, hazard, or mixed prospect and refuge; the fifth group viewed no image. The Short-Form McGill Pain Questionnaire and the Profile of Mood States survey instruments were used to assess pain and mood, respectively. Continuous physiological readings of heart rate and blood pressure were collected. Pain was induced through a cold pressor task, which required participants to immerse their nondominant hand in ice water for up to 120 seconds. RESULTS: The mixed prospect and refuge image treatment showed significantly lower sensory pain responses, and the no-image treatment indicated significantly higher affective pain perception responses. The hazard image treatment had significantly lower diastolic blood pressure readings during the pain treatment, but it also had significantly high total mood disturbance. CONCLUSIONS: Although there was no clear "most" therapeutic image, the mixed prospect and refuge image showed significant potential to reduce sensory pain. The hazard image was the most effective at distracting participants from pain, but it should not be considered a positive distraction because it also received the highest mood disturbance scores of all groups.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".