Increasing the awareness of overweight among Saudi people using different social media networks such as Twitter and Snapchat: A Case of PSAU
Bibliographic record
Abstract
The development of the internet infrastructure has generated a new phenomenon and lead to some changes in business strategies as it allowed two-way communications among consumers and companies.At present, social media play a key role in communicating information to people around the world and clearly play a vital role in increasing interaction and awareness in an important way.At present, awareness of diseases has disappeared with new methods.Social networking sites like Twitter, Snapchat and Facebook may change the roles the meanings of information delivery and play a superior in that scope.This research investigates the main factors of the power of social media activities (customization, and e-WOM) towards increasing Saudi's awareness on using mhealthcare apps.The results indicate that customization had a positive and direct influence on increasing Saudi's awareness towards using m-healthcare apps (Brand Awareness).e-WOM had a positive and direct influence on increasing Saudi's awareness toward using m-healthcare apps (brand awareness), confirming that the power of social media activities was an imperative contributor to brand equity (brand awareness).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".