Identifying Strategies for Home Management of Ostomy Care: Content Analysis of YouTube
Bibliographic record
Abstract
BACKGROUND: The social media platform YouTube is a recognized educational resource for health information, but few studies have explored its value for conveying the lived experience of individuals managing chronic health conditions and end users' interactions with medical device technology. Our study explores self-care strategies and end user needs of people living with a stoma because patient education and engagement in ostomy self-care are essential for avoiding ostomy-related complications. Ostomy surgery creates a stoma (an opening) in the abdomen to alter the route of excreta from digestive and urinary organs into a detachable external pouching system. After hospital discharge, people who have undergone ostomies perform critical self-care tasks including frequent ostomy appliance changes and stomal and peristomal skin maintenance. OBJECTIVE: The purpose of this study was to systematically assess YouTube videos narrated by people who have undergone ostomies about their ostomy self-care in home (nonhospital) settings with a focus on identifying end user needs and different strategies used by people who have undergone ostomies during critical self-care tasks. METHODS: Using predefined search terms and clear inclusion and exclusion criteria, we identified YouTube videos depicting narrators who have undergone ostomies and their ostomy self-care in home settings. Using a consensus coding approach among 3 independent reviewers, all videos were analyzed to collect metadata, data of narrators who have undergone ostomies, and specific content data. RESULTS: There were 65 user-generated YouTube videos that met the inclusion and exclusion criteria. These videos were posted by 28 unique content creators representing a broad range of ages who used a variety of supplies. The common challenges discussed were peristomal skin complications, inadequate appliance adhesion and subsequent leakage, and supplies-related challenges. Narrators who have undergone ostomies discussed various expert tricks and tips to successfully combat these challenges. CONCLUSIONS: This study used a novel approach to gain insights about end user interactions with medical devices while performing ostomy self-care, which are difficult to gain using traditional behavioral techniques. The analysis revealed that people who have undergone ostomies are willing to share their personal experience with ostomy self-care on the web and that these videos are viewed by the public. User-generated videos demonstrated a variety of supplies used, end user needs, and different strategies for performing ostomy self-care. Future research should examine how these findings connect to YouTube ostomy self-care content generated by health care professionals and organizations and to guidelines for ostomy self-care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".