TikTok and #OccupationalTherapy: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Medical providers use the short-form video social media platform TikTok to share information related to their scope of practice and insights about their professions. Videos under the hashtag #occupationaltherapy on TikTok have over 100 million views, but there is no evidence investigating how occupational therapy information and knowledge are shared on the platform. OBJECTIVE: The purpose of this cross-sectional study is to describe TikTok content with the hashtag #occupationaltherapy and investigate how occupational therapy is portrayed. METHODS: We performed a content analysis on the top 500 TikTok videos under the hashtag #occupationaltherapy. We analyzed occupational therapy content themes (occupational therapy intervention, education, student training, universal design, and humor), practice settings (pediatrics, generalists, dementia, hand therapy, neurology, occupational therapy students, older adults, mental health, and unknown), and sentiments (positive, negative, and neutral). RESULTS: The videos in our sample (n=500) received 175,862,994 views. The 2 most prevalent content areas were education (n=210) and occupational therapy interventions (n=146). The overall sentiment of the videos was positive (n=302). The most frequently observed practice settings in the videos were pediatrics (n=131) and generalists (n=129). Most videos did not state that it was occupational therapy (n=222) or misused the hashtag (n=131). CONCLUSIONS: TikTok has the potential for occupational therapists to share innovations, build communities of practice, and engage in collaborative efforts to share information about occupational therapists' unique roles with diverse populations. Future research is needed to monitor the quality of information and debunk inaccuracies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".