Social Media Strategies for Health Promotion by Nonprofit Organizations: Multiple Case Study Design
Bibliographic record
Abstract
BACKGROUND: Nonprofit organizations have always played an important role in health promotion. Social media is widely used in health promotion efforts. However, there is a lack of evidence on how decisions regarding the use of social media are undertaken by nonprofit organizations that want to increase their impact in terms of health promotion. OBJECTIVE: The aim of this study was to understand why and how nonprofit health care organizations put forth social media strategies to achieve health promotion goals. METHODS: A multiple case study design, using in-depth interviews and a content analysis of each social media strategy, was employed to analyze the use of social media tools by six North American nonprofit organizations dedicated to cancer prevention and management. RESULTS: The resulting process model demonstrates how social media strategies are enacted by nonprofit organizations to achieve health promotion goals. They put forth three types of social media strategies relative to their use of existing information and communication technologies (ICT)-replicate, transform, or innovate-each affecting the content, format, and delivery of the message differently. Organizations make sense of the social media innovation in complementarity with existing ICT. CONCLUSIONS: For nonprofit organizations, implementing a social media strategy can help achieve health promotion goals. The process of social media strategy implementation could benefit from understanding the rationale, the opportunities, the challenges, and the potentially complementary role of existing ICT strategies.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".