Support for My Video is Support for Me:
Bibliographic record
Abstract
OBJECTIVES: Adolescents are heavy users of social media as a venue to share experience and obtain information. Adolescents with chronic pain may be no different. Given that adolescents with chronic pain report feelings of social isolation, of being different, and lack peer understanding, social media may help them obtain social support. We conducted a scoping review of YouTube to identify how adolescents with chronic pain use this platform to connect with other adolescents. MATERIALS AND METHODS: The terms "youth with chronic pain" and "teens with chronic pain" were entered into the YouTube search bar to locate videos. Videos in English, targeted at and including an adolescent with chronic pain were included. Videos were screened for eligibility until 20 consecutive videos listed on the main page were excluded. For each included video the first 5 related videos suggested by YouTube in the sidebar were also screened for eligibility. RESULTS: This selection process resulted in 18 included videos, with a total of 936 viewer comments. Recurring comment themes were identified using qualitative content analysis. Video content mainly covered multidisciplinary treatment options, alternative treatments, and impact of pain on daily life. Although a variety of treatment options were discussed, details of treatment were lacking. Comments reflected the overarching message "you are not alone!" and mainly focused on providing and receiving support, sharing suffering, and revealing the impact of pain on relationships and daily life. DISCUSSION: Despite potential challenges associated with social media, YouTube may be a promising platform for provision of social support for adolescents with chronic pain.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.022 |
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".