Evaluating YouTube as a Source of Patient Education on the Role of the Hospitalist: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hospital medicine is a relatively new specialty field, dedicated to the delivery of comprehensive medical care to hospitalized patients. YouTube is one of the most frequently used websites, offering access to a gamut of videos from self-produced to professionally made. OBJECTIVE: The aim of our study was to determine the adequacy of YouTube as an effective means to define and depict the role of hospitalists. METHODS: YouTube was searched on November 17, 2014, using the following search words: "hospitalist," "hospitalist definition," "what is the role of a hospitalist," "define hospitalist," and "who is a hospitalist." Videos found only in the first 10 pages of each search were included. Non-English, noneducational, and nonrelevant videos were excluded. A novel 7-point scoring tool was created by the authors based on the definition of a hospitalist adopted by the Society of Hospital Medicine. Three independent reviewers evaluated, scored, and classified the videos into high, intermediate, and low quality based on the average score. RESULTS: A total of 102 videos out of 855 were identified as relevant and included in the analysis. Videos uploaded by academic institutions had the highest mean score. Only 6 videos were classified as high quality, 53 as intermediate quality, and 42 as low quality, with 82.4% (84/102) of the videos scoring an average of 4 or less. CONCLUSIONS: Most videos found in the search of a hospitalist definition are inadequate. Leading medical organizations and academic institutions should consider producing and uploading quality videos to YouTube to help patients and their families better understand the roles and definition of the hospitalist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".