Social Media Technology and Public Health in Ontario: Findings from a Planning Meeting Exploring Current Practices and Future Research Directions
Bibliographic record
Abstract
In the province of Ontario, many of the public health units (PHUs) now possess and use social media as part of their daily health promotion and communication operations. To explore this topic, a planning meeting was held to generate deeper insights toward the use of these forms of technology for preventative services delivery. The planning meeting was held with 50 participants, comprising representatives from 20 of the 36 PHUs in Ontario, interested academics, students and government representatives. A nominal group technique (NGT) was used to build consensus related to future research needs, as related to public health and social media. Participants generated a range of insights around the use of social media, including the need for: leadership buy-in and resource allocation; social media policy and governance structure; performance measurement and evaluation; practices related to engagement with program recipients and addressing the lack of resources faced by many health units. Future research priorities were also generated, related to evaluating the cost-benefit of social media activities and understanding behaviour change implications. Further research is needed to evaluate the functionality, leadership and competency requirements and impact(s) of these new forms of health communication technology within public health service delivery.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".