Insights Into Dialysis Initiation: A Foucauldian Discourse Analysis of the r/dialysis Reddit Forum
Bibliographic record
Abstract
Patients on dialysis develop unique relationships with their providers, fellow patients, and the broader healthcare system. This network of relationships is a well-established key factor influencing both their healthcare experience and mortality rates. Yet, despite ongoing efforts to improve these networks of relationships, health outcomes remain unfavorable for dialysis patients. Investigating the formation of these relationships through a social media platform provides valuable insight into patients lived experiences, shedding light on pervasive power dynamics as seen from the patient perspective while addressing methodological gaps present in the literature. Analyzing social media platforms helps identify critical areas for improvement that may have been overlooked to enhance the experience and outcomes of patients on dialysis. In this paper, we use Foucauldian discourse analysis to examine 41 posts and their associated comments from the r/dialysis forum on Reddit, focusing on the first year of dialysis initiation and exploring how societal discourse shapes and is shaped by peer-to-peer interactions, impressions, communities, and frameworks. In this study, we highlight how power and resistance are reflected in the discursive choices made by patients, as well as its influence on their conceptualizations of the patient-provider relationship as they begin their dialysis journey. These insights add to the dearth of current literature that use social media platforms and discourse analysis in investigating healthcare experiences. They lay the groundwork for better supporting a vulnerable clinical population and provide a foundation for future academic research using this methodology.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".