Understanding compassionate care from the patient perspective: Highlighting the experience of head and neck cancer care
Bibliographic record
Abstract
Objectives: To address the knowledge gap in the practice of compassionate healthcare by elucidating patient perspectives on compassion, empathy, and sympathy. Methods: Semi-structured telephone interviews were conducted at two time points with patients undergoing head and neck cancer treatment. Questions explored participants' understanding of compassion, sympathy, and empathy, as they relate to each other and to healthcare. Interviewers manually recorded responses. Qualitative exploratory methods were used to analyze data; inductive line-by-line coding was conducted to develop primary codes. Themes emerged through categorization of codes. Results: Ninety-five interviews conducted with 63 participants across two time points revealed four major themes - Compassion-vs-Empathy-vs-Sympathy, Coping Methods, Showing Care, and Nature of Interaction - encompassing seven categories, with a total of 24 codes. Codes were consistent across time points, except for two new codes, "positivity" and "personalized" emerging during follow-up interviews. Conclusions: Patient narrative from this study supported the concept that compassion is multidimensional and enabled several dimensions to be identified, highlighting the importance of patient perspectives in improving the provision of compassionate healthcare. Findings should be considered in future training and practice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".