Hand therapy in Iran during the pandemic of covid-19: Scraping Social media (Instagram)
Bibliographic record
Abstract
Background: The fast-evolving pandemic of COVID-19 has forced clinicians to implement tele-health strategies in their routine practice. Social media provides unprecedented opportunities to transfer educational, monitoring, and individualizing data to the target populations. There have been numerous efforts on social media to use telerehabilitation approaches for patients and therapists. Question/purpose :The purpose of this study was to explore and analysis the trend that hand therapists used for tele-rehabilitation approaches during the lock dawn period in Iran. Methods :Scraping method was used to map out the tele-rehabilitation strategies that Iranian therapists have implemented for the hand and upper extremity injuries during the COVID-19 pandemic. Tele rehabilitation method was searched by relevant hashtags and direct contact with therapists. Extracted data were described and categorized by content analysis and thematic coding. Results: During lock dawn period, 27 records from 18 accounts were posted with relative tele rehabilitation content in Iran. Based on the content of extracted data four themes were conceptualized: Empowerment, informative, adaptive to new situations, and supportive approach. The content that were covered in the most posts were informative approach (40%). Conclusion: In spite of the urgent necessity for delivering care during the lockdown, the total number of the active therapists was very low. No documented method or platform was identified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".