Living with advanced cancer: Rich Pictures as a means for health care providers to explore the experiences of advanced cancer patients
Bibliographic record
Abstract
BACKGROUND: To provide holistic care to patients with advanced cancer, health care professionals need to gain insight in patients' experiences across the different domains of health. However, describing such complex experiences verbally may be difficult for patients. The use of a visual tool, such as Rich Pictures (RPs) could be helpful. We explore the use of RPs to gain insight in the experiences of patients with advanced cancer. METHODS: Eighteen patients with advanced cancer were asked to draw a RP expressing how they experienced living with cancer, followed by a semi-structured interview. Qualitative content analysis, including the examination of all elements in the drawings and their interrelationships, was used to analyze the RPs, which was further informed by the interviews. RESULTS: The RPs clearly showed what was most important to an individual patient and made relations between elements visible at a glance. Themes identified included: medical aspects, the experience of loss, feelings related to loss, support from others and meaningful activities, and integration of cancer in one's life. The added value of RPs lies in the ability to represent these themes in one single snapshot. CONCLUSIONS: RPs allow for a complementary view on the experiences of advanced cancer patients, as they show and relate different aspects of patients' lives. A RP can provide health care professionals a visual summary of the experiences of a patient. For patients, telling their story to health care professionals might be facilitated when using RPs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".