Communicating the experience of chronic pain through social media: patients’ narrative practices on Instagram
Bibliographic record
Abstract
Background: The use of technologies in health is changing the relationship between patients and their conditions. In the case of chronic pain, social media are offering these individuals a new way to talk about their experience. The objective of this paper is to identify how and why patients are using these online platforms for pain communication. Particularly, this study analyses self-expression practices of this disease on Instagram.Method: A sample of posts was selected from the platform following a multistage sampling strategy (n = 350). These publications were examined through a qualitative analysis based on multiple categories related to the experience of chronic pain.Results: Patients are using Instagram to give visibility to illnesses such as fibromyalgia or endometriosis. Moreover, these individuals talk about their condition in terms of chaos and uncertainty, using the narratives to construct a world that gives meaning to their chronic pain. However, men are largely absent from these online practices. Ninety-four percent of the publications included in the sample were shared by women (n = 329).Conclusion: Findings indicate that Instagram is changing the way patients live with their chronic pain. Considering the complexity of this condition, care providers could improve the assessment of chronic pain by paying more attention to the self-expression practices of these individuals.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".