Nature Photographs as Complementary Care in Chemotherapy: A Randomized Clinical Trial
Bibliographic record
Abstract
The incidence of cancer cases is increasing worldwide, and chemotherapy is often necessary as part of the treatment for many of these cases. Nature-based interventions have been shown to offer potential benefits for human well-being. Objective: This study aims to investigate the outcome of nature images on clinical symptom management related to chemotherapy. Methods: A randomized clinical trial was conducted in an outpatient cancer unit of a private hospital in Brazil, with 173 participants over the age of 18 who were undergoing chemotherapy and had signed an informed consent form. The intervention consisted of the presentation of a 12-min video featuring nature images categorized under the themes of Tranquility, Beauty, Emotions Up, or Miscellany. Images were sourced from the e-Nature Positive Emotions Photography Database (e-NatPOEM), a publicly available collection of affectively rated images. Sociodemographic and clinical data, as well as the participants’ connection to nature, were investigated. The Positive Affect/Negative Affect Scale (PANAS) and the Edmonton Symptom Assessment System (ESAS) were applied pre- and post-intervention. Results: Data showed very strong evidence of a reduction in negative affect for the intervention group (p < 0.001) and moderate evidence for the control group (p = 0.034). There was also a significant reduction in the intervention group for pain (p < 0.001), tiredness (p = 0.002), sadness (p < 0.001), anxiety (p < 0.001), and appetite (p = 0.001). The Beauty video had the best performance, while the Tranquility video showed no significant improvement in any of the symptoms evaluated. These findings suggest that images of nature may be a valuable tool to help control clinical and psychological symptoms in cancer patients undergoing chemotherapy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".