“Give My Daughter the Shot!”: A Content Analysis of the Depiction of Patients with Cancer Pain and Their Management in Hollywood Films
Bibliographic record
Abstract
INTRODUCTION: Cinemeducation, the pedagogical use of films, has been used in a variety of clinical disciplines. To date, no studies have looked at the use of film depictions of cancer pain and its management in clinical education. We investigated how patients with cancer pain and their management are depicted in Hollywood films to determine whether there is content that would be amenable to use for cancer pain assessment and management education. METHODS: A qualitative content analysis was performed. Films that contained characters with or references to cancer pain were searched for using the International Movie Database, the Literature Arts Medicine Database, the History of Medicine and Medical Humanities Database, and Medicine on Screen. After review, 4 films were identified for review and analysis. RESULTS: Themes that emerged from the analysis concerned the films' depictions of characters with pain, their healthcare providers, the therapies used for pain management, and the setting in which pain management was provided. CONCLUSIONS: This study demonstrates that patients with cancer pain are depicted in a compassionate manner. Pain management focused on the use of opioids. The settings in which patients received pain management was depicted as not being amenable to providing holistic care. This variety of topics related to pain management covered in the films make them amenable to use in cinemeducation. This study therefore forms the basis for future work developing film-based cancer education modules.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".