Implications of Age on Social Media Utilization in Health Care Practice Development: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Medical practices, which are businesses through which one or more physicians treat patients, have likely not yet taken full advantage of the reach of social media. This study analyzed data collected using an anonymous survey to assess the potential utilization of large, established social media platforms in health care. The survey collected data from a diverse population of health care professional students, faculty, and physicians affiliated with the Texas Tech University Health Sciences Center (TTUHSC). This study provides significant, actionable data to more efficiently implement a social media strategy focused on age to help developing private practices and outpatient clinics from the perspective of those with experience in the field of medicine. OBJECTIVE: This cross-sectional, exploratory, descriptive study aims to explore the most effective strategies to use social media based on patient age to bring further success to a medical practice. METHODS: Data were gathered from an anonymous, peer-validated Qualtrics survey created by the corresponding authors based on the recommendations from a panel of experts including executive leadership at TTUHSC. The survey used a variety of question styles to measure differences between social media platforms, including frequency of use, current and future implications in medicine, and comfort in a health care setting. The sample population included students, interns, faculty, and physicians affiliated with the TTUHSC located throughout West Texas. RESULTS: The anonymous survey included 673 individuals from several different age groups predetermined at the beginning of the study. There were 154 respondents aged between 18 and 25 years, 171 aged between 26 and 35 years, 133 aged between 36 and 45 years, 104 aged between 46 and 55 years, and 111 aged between 56 and 89 years. The sample population also has a variety of educational achievements. The respondents were grouped based on the highest level of education attained, and this included 23.5% (n=158) of respondents who earned a high school diploma, 42% (n=283) who earned a bachelor's degree, 17.1% (n=115) who earned a master's degree, and 17.4% (n=117) who earned a doctorate degree. CONCLUSIONS: As social media continues to gain momentum, efficient utilization of the available platforms can help medical practices achieve larger patient populations and deliver more personalized care. However, privacy and security concerns should be considered while using social media in health care settings. Although this study demonstrated overwhelming interest in using social media in the medical field across all age groups, adoption willingness appears to be higher in younger respondents than in older respondents. Facebook was the most widely accepted social media platform in health care settings among all age groups. Nonetheless, other social media platforms could potentially be used more effectively depending on the age range of the targeted patient population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".