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Record W4296555324 · doi:10.1080/24740527.2022.2123308

Popcorn in the pain clinic: A content analysis of the depiction of patients with chronic pain and their management in motion pictures

2022· article· en· W4296555324 on OpenAlexaff
Karim Mukhida, Sina Sedighi, Catherine Hart

Bibliographic record

VenueCanadian Journal of Pain · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDepictionChronic painPain managementMotion (physics)Content (measure theory)Content analysisPsychologyMedicinePsychotherapistPhysical therapyArtComputer scienceSociologyArtificial intelligenceVisual artsMathematicsAnthropology

Abstract

fetched live from OpenAlex

The watching of films is popular and accessible to broad segments of the population. The depiction of medical conditions in films has the potential to affect the public's perception of them and contribute to stereotypes and stigma. We investigated how patients with chronic pain and their management are depicted in feature films. Films that contained characters with or references to chronic pain were searched for using databases such as the International Movie Database. Themes that emerged from the content analysis revolved around the films' depictions of characters with pain, their health care providers, and therapies for pain management. Patients with chronic pain were depicted in various ways, including in manners that could elicit empathy from audiences or that might contribute to the development of negative stereotypes about them. The attitudes of health care professionals toward patients with chronic pain ranged from compassionate to dispassionate. Pain management was typically depicted as lacking in breadth or using multidisciplinary approaches with a focus on pharmacological management. The variety of topics related to chronic pain depicted in feature films lends to their use in medical education strategies to better inform health care professions trainees about chronic pain management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.327
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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