GREEN EXPERIENCES AND HEALTH IN INDIVIDUALS WITH HEAD AND NECK CANCER: ASSESSING CHANGES IN DIRECTED ATTENTION, AFFECT, AND SYMPTOM DISTRESS
Bibliographic record
Abstract
Introduction: This study explored whether green experiences (i.e., human-nature\ninteractions) provided via DVD slideshows could produce changes in directed attention and affect in individuals receiving chemotherapy for head and neck cancer. Based on the theoretical frameworks of attention restoration theory (ART; R. Kaplan & S. Kaplan,\n1989) and Ulrich’s (1983) psychoevolutionary framework (PET), it was anticipated that the restorative effects of natural restorative environments (REs) could be effective in reducing directed attention fatigue, affective manifestations of stress, and decrease symptom distress secondary to chemotherapeutic treatments. Methods: 5 participants (4 males, 1 female) with a primary diagnosis of head and neck cancer were included in the present study. DVD-based slideshows of REs were used as stimuli to produce green experiences in the home setting. Outcome measures: Data were collected using the Necker Cube Pattern Control test (NCPC), Zuckerman Inventory of Personal Reactions (ZIPERS), and Edmonton Symptom Assessment System (ESAS). Results: Results indicate high within subject and between subject variability for all measures. Measures of symptom distress were found to be highly variable across all subjects, but generally increased in a consistent fashion following chemotherapy. Conclusion: DVDs were successfully used in the “home setting” to transmit a green experience; however, further investigation is warranted to fully test the restorative potential of green experiences in this population.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".