Environmental scan of social media usage among Ontario public health units
Bibliographic record
Abstract
Social media is a relatively novel way to send and express information to a large number of people at one time. Public health units (PHUs) have started to use this forum, but there has been almost no research done on how it is being used and if the way information is presented is effective. Engagement is one way to track if something posted on a social media website is being disseminated effectively to the intended audience. If a post has a low level of engagement this means that less people saw it and read the information regardless of how many times it was posted or how much work was put into the post. This article summarizes an environmental scan completed to assess engagement from all Ontario’s PHUs’ usage of social media from January 1, 2020, to June 30, 2020. The social media platforms assessed in this scan are Facebook, Instagram, Twitter, YouTube, Tiktok, Pinterest, and Reddit. All social media platforms except Reddit are being used by PHUs in Ontario. Twenty-nine PHUs have their own social media profiles, and five have their profiles included in their municipality’s. Engagement varies by region and platform, but in general increased posting (especially on Twitter and Facebook) had a small negative effect on engagement. Posting less often but with more original or creative content leads to better engagement. Increased follower counts also accounted for higher engagement across all platforms. PHUs would best use their resources to increase follower count and post less often and include more personalized posts to effectively spread their information.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".