Quality of Life for Our Patients: How Media Images and Messages: Influence Their Perceptions
Bibliographic record
Abstract
Media messages and images shape patients' perceptions about quality of life (QOL) through various "old" media-literature, film, television, and music-and so-called "new" media-the Internet, e-mail, blogs, and cell phones. In this article, the author provides a brief overview of QOL from the academic perspectives of nursing, psychology, behavioral medicine, multicultural studies, and consumer marketing. Selected theories about mass communication are discussed, as well as new technologies and their impact on QOL in our society. Examples of media messages about QOL and the QOL experience reported by patients with cancer include an excerpt from the Canadian Broadcasting Corporation radio interview with author Carol Shields, the 60 Minutes television interview focusing on Elizabeth Edwards (wife of presidential candidate John Edwards), and an excerpt from the 1994 filmThe Shawshank Redemption. Nurses are challenged to think about how they and their patients develop their perceptions about QOL through the media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".