How are healthcare institutions using Facebook to interact with online communities? Results from a case study in Central Pennsylvania
Bibliographic record
Abstract
Social media tools and applications are increasingly being integrated into modern medicine. However, little is known about how healthcare institutions are interacting online with their populations. In this case study, we identified a convenience sample of 11 institutions in Central Pennsylvania with Facebook Pages and evaluated their interactions with online communities. From May-June 2013, we noted type of healthcare institution (e.g. hospital, family practice); number of overall “likes” accrued by the healthcare facility; number of overall posts and “likes”, “comments”, and “content shares” associated with those posts; as well as number of location check-ins by “followers”. We thematically categorized each institutional post. Average number of Facebook Page “likes” was 2,261, and average number of overall posts was 28.9, or about one post every three days. On average, each post generated 16 “likes”, 1 comment, and 2.4 shares. Average number of location “check-ins” by visiting patients was 6,348. Most commonly published content across all Pages was advertisements (89%) and institutional news (89%). Patient populations in Central Pennsylvania are seeking out healthcare institutions on Facebook, although most communication appears unidirectional and involves institutional advertising and promotion. There are opportunities for institutions to focus on health promotion and undertake “social” preventive health strategies using social media.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".