Social Media Technology and Public Health in Ontario: Findings from a Planning Meeting Exploring Current Practices and Future Research Directions
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
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.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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