Plausibility of Using a Checklist With YouTube to Facilitate the Discovery of Acute Low Back Pain Self-Management Content: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Access to guideline-consistent effective care for acute low back pain (ALBP) is generally regarded as limited. Researchers have recognized the potential of YouTube as a clinical and patient education resource that may improve access to appropriate care. However, the heterogeneity of evaluation approaches and variable quality of health information have generally limited the potential of YouTube as a self-management intervention. OBJECTIVE: This study aims to increase the understanding of ALBP content available on YouTube in 2020 and to establish the plausibility of using a simple checklist to facilitate the discovery of YouTube content consistent with current guidelines. We examined the following 4 research questions: how was the data set defined, what are the metadata characteristics of the videos in the data set, what is the information quality of ALBP YouTube videos, and what are the characteristics of the YouTube data set based on an ALBP self-management checklist? METHODS: This was an exploratory, qualitative infodemiology study. We identified videos in our data set through YouTube search based on popular ALBP-relevant search terms identified through Google Trends for YouTube. We accessed YouTube metadata using the YouTube data tools developed by the University of Amsterdam. We used a modified Brief DISCERN checklist to examine the information quality. We developed a checklist based on the 2018 Lancet Low Back Pain guidelines to examine self-management content. RESULTS: We analyzed a data set of 202 YouTube videos authored by chiropractors, physicians, physiotherapists, and instructors of yoga and other disciplines. We identified clear differences in the ALBP videos in our data set based on the authors' disciplines. We found that the videos authored by each discipline strongly featured a specific intervention domain, that is, education, treatment, or exercise. We also found that videos authored by physicians were consistently coded with the highest ALBP self-management content scores than all other disciplines. CONCLUSIONS: The results returned by YouTube in response to a search for back pain-related content were highly variable. We suggest that a simple checklist may facilitate the discovery of guideline-concordant ALBP self-management content on YouTube. Further research may identify the clinical contexts in which the use of an ALBP checklist with YouTube is feasible.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".