Social Media for the Promotion of Holistic Self-Participatory Care: An Evidence Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: As health information is becoming increasingly accessible, social media offers ample opportunities to track, be informed, share and promote health. These authors explore how social media and holistic care may work together; more specifically however, our objective is to document, from different perspectives, how social networks have impacted, supported and helped sustain holistic self-participatory care. METHODS: A literature review was performed to investigate the use of social media for promoting health in general and complementary alternative care. We also explore a case study of an intervention for improving the health of Greek senior citizens through digital and other means. RESULTS: The Health Belief Model provides a framework for assessing the benefits of social media interventions in promoting comprehensive participatory self-care. Some interventions are particularly effective when integrating social media with real-world encounters. Yet not all social media tools are evidence-based and efficacious. Interestingly, social media is also used to elicit patient ratings of treatments (e.g., for depression), often demonstrating the effectiveness of complementary treatments, such as yoga and mindfulness meditation. CONCLUSIONS: To facilitate the use of social media for the promotion of complementary alternative medicine through self-quantification, social connectedness and sharing of experiences, exploration of concrete and abstract ideas are presented here within. The main mechanisms by which social support may help improve health - emotional support, an ability to share experiences, and non-hierarchal roles, emphasizing reciprocity in giving and receiving support - are integral to social media and provide great hope for its effective use.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it