Exploring the patient experience of chronic illness through literature and film
Bibliographic record
Abstract
Objective: Caring for patients with a chronic illness requires a holistic approach. To help RN-BSN nursing students in a course focusing on chronic illness see the patient as a unique individual and to foster empathy by emphasizing the person behind each patient, literature and film were used to make the patient’s lived experience of chronic illness real.Methods: Students read autobiographies or biographies and viewed film about the patient experience of chronic illness. Materials were selected based on the illness and patient issues described. Using the Socratic Seminar, students prepared questions based on weekly reading assignments; faculty prepared questions for each film, encouraging active participation and group discussion of underlying insights. Many discussions revolved around select film scenes or quotations students selected from the text and the comparison to their practice experience with similar patients in the clinical setting.Results: Satisfaction with the course was overwhelming, with all students rating every evaluative category as a “5”, strongly agreeing that the course advanced their empathy towards patients with chronic illness. Comments detailed students’ having a better understanding of how chronic illness truly affects the patient and family. The books resonated with the class more than the films, as the books had more detail and put the reader into the setting as if they were the person.Conclusions: The use of literature and film is an effective means to enhance RN-BSN nursing students’ ability to understand and empathize with a patient’s needs and was successful in helping students understand the patient experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".