User‐driven conversations about dialysis through Facebook: A qualitative thematic analysis
Bibliographic record
Abstract
AIM: As one of the most popular social networking sites in the world, Facebook has strong potential to enable peer support and the user-driven sharing of health information. We carried out a qualitative thematic analysis of the wall posts of a public Facebook group focused on dialysis to identify some of the major themes discussed. METHODS: We searched Facebook using the word 'dialysis'. A Facebook group (Dialysis Discussion Uncensored) with the highest number of members was selected amongst publicly available forums related to dialysis and operated in English (http://www.facebook.com/groups/DialysisUncensored). Two researchers independently extracted information on features of the group including purpose, group members and the user-generated posts on the group wall. Posts were further analysed to develop major themes. RESULTS: Characteristics of a Facebook group based on its participants and activities are presented. Three themes are described with representative quotations. In a period of 2 weeks, we found 1257 wall posts with total of 31 636 likes and 15 972 comments. All messages were in English, and the majority of the participants were dialysis patients. However, we observed the participation of family members and care providers as well. Posts were categorized into three major themes: sharing information, seeking and providing emotional and social support and sharing experience. CONCLUSION: Findings of this study provide an example of how a social networking platform can enable patients and their families to share information and to encourage peer-based support for managing dialysis-related experiences.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".