The “Walking for Harm Reduction Through Street Engagement” Social Media Knowledge Translation Strategy
Bibliographic record
Abstract
ABSTRACT Due to its ability to cost-effectively reach underrepresented groups, social media is a valuable tool for facilitating knowledge translation. Consequently, a Social Media Knowledge Translation Strategy was developed for the Walking for Harm Reduction Through Street Engagement (WHiSE 2.0) study; a research project that seeks to identify the harm reduction approaches desired and used by Indigenous people in Thunder Bay, Sault Ste. Marie, and Sudbury. The strategy aided in the referral of harm reduction services to Indigenous people in the WHiSE 2.0 study sites, build relationships between Indigenous communities and harm reduction organizations, and increase knowledge translation and exchange (KTE) efforts among a target audience. The Social Media Knowledge Translation Strategy was developed following Elliot et al.’s [14] social media knowledge translation stages of planning, doing, and evaluating, and quantitative data was collected from an Instagram and Twitter account between May 2023 and August 2023. The results showed a disproportionate ratio of new followers to reach, content interactions, and profile visits. Additionally, there was higher reach and impressions compared to engagement, and the aspiration of amassing 150 followers on Instagram, Facebook, and Twitter which was not realized. However, as any number of impressions, profile visits, content interactions, reach, engagement, and new followers beyond 0, was viewed as successfully disseminating knowledge to at least one organization or individual, the strategy successfully engaged the intended target audience. In future iterations of the strategy, metrics beyond social media insights must be collected to fully evaluate the success of the strategy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".