The Information Needs and Experiences of People Living With Cardiac Implantable Electronic Devices: Qualitative Content Analysis of Reddit Posts
Bibliographic record
Abstract
BACKGROUND: Cardiac implantable electronic devices (CIEDs) are used to treat a range of cardiovascular diseases and can lead to substantial clinical improvements. However, studies evaluating patients' experiences of living with these devices are sparse and have focused mainly on implantable cardioverter defibrillators. In addition, there has been limited evaluation of how people living with a CIED use social media to gain insight into their condition. OBJECTIVE: This study aims to analyze posts from web-based communities called subreddits on the website Reddit, intended for people living with a CIED, to characterize the informational needs and experiences of patients. METHODS: Reddit was systematically searched for appropriate subreddits, and we found 1 subreddit that could be included in the analysis. A Python-based web scraping script using the Reddit application programming interface was used to extract posts from this subreddit. Each post was individually screened for relevancy, and a register of participants' demographic information was created. Conventional qualitative content analysis was used to inductively classify the qualitative data collected into codes, subcategories, and overarching categories. RESULTS: Of the 484 posts collected using the script, 186 were excluded, resulting in 298 posts from 196 participants being included in the analysis. The median age of the participants who reported this was 33 (IQR 22.0-39.5; range 17-72) years, and the majority had a permanent pacemaker. The content analysis yielded 5 overarching categories: use of the subreddit by participants, questions and experiences related to the daily challenges of living with a CIED, physical sequelae of CIED implantation, psychological experiences of living with a CIED, and questions and experiences related to health care while living with a CIED. These categories provided insight into the diverse experiences and informational needs of participants living with a CIED. The data predominantly represented the experiences of younger and more physically active participants. CONCLUSIONS: Social media provides a platform through which people living with a CIED can share information and provide support to their peers. Participants generally sought information about the experiences of others living with a CIED. This was often done to help overcome a range of challenges faced by participants, including the need to adapt to living with a CIED, difficulties with navigating health care, psychological difficulties, and various aversive physical sequelae. These challenges may be particularly difficult for younger and physically active people. Health care professionals may leverage peer support and other aid to help people overcome the challenges they face while living with a CIED.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".