“It’s Totally Okay to Be Sad, but Never Lose Hope”: Content Analysis of Infertility-Related Videos on YouTube in Relation to Viewer Preferences
Bibliographic record
Abstract
BACKGROUND: Infertility patients frequently use the internet to find fertility-related information and support from people in similar circumstances. YouTube is increasingly used as a source of health-related information and may influence health decision making. There have been no studies examining the content of infertility-related videos on YouTube. OBJECTIVE: The purpose of this study was to (1) describe the content of highly viewed videos on YouTube related to infertility and (2) identify video characteristics that relate to viewer preference. METHODS: Using the search term "infertility," the 80 top-viewed YouTube videos and their viewing statistics (eg, views, likes, and comments) were collected. Videos that were non-English, unrelated to infertility, or had age restrictions were excluded. Content analysis was used to examine videos, employing a coding rubric that measured the presence or absence of video codes related to purpose, tone, and demographic and fertility characteristics (eg, sex, parity, stage of fertility treatment). RESULTS: A total of 59 videos, with a median of 156,103 views, met the inclusion criteria and were categorized into 35 personal videos (35/59, 59%) and 24 informational-educational videos (24/59, 41%). Personal videos did not differ significantly from informational-educational videos on number of views, dislikes, subscriptions driven, or shares. However, personal videos had significantly more likes (P<.001) and comments (P<.001) than informational-educational videos. The purposes of the videos were treatment outcomes (33/59, 56%), sharing information (30/59, 51%), emotional aspects of infertility (20/59, 34%), and advice to others (6/59, 10%). The tones of the videos were positive (26/59, 44%), neutral (25/59, 42%), and mixed (8/59, 14%); there were no videos with negative tone. No videos contained only male posters. Videos with a positive tone did not differ from neutral videos in number of views, dislikes, subscriptions driven, or shares; however, positive videos had significantly more likes (P<.001) and comments (P<.001) than neutral videos. A majority (21/35, 60%) of posters of personal videos shared a pregnancy announcement. CONCLUSIONS: YouTube is a source of both technical and personal experience-based information about infertility. However, videos that include personal experiences may elicit greater viewer engagement. Positive videos and stories of treatment success may provide hope to viewers but could also create and perpetuate unrealistic expectations about the success rates of fertility treatment.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".