Social media messaging by Canadian health organizations to address indoor tanning during policy and scientific shifts
Bibliographic record
Abstract
Indoor tanning (IT) is a modifiable risk factor for skin cancer, including melanoma. Social media provides a large potential audience for messaging to address IT, and incorporating elements of evidence and theory-based message design may be an effective way to impact knowledge, attitudes, and behaviours within these audiences. However, few studies have investigated whether these elements are used in practice. This content analysis explored whether Canadian health and cancer organizations incorporate theory- and evidence-supported message design strategies and provides suggestions for how these messages could be strengthened. We identified 36 Facebook pages operated by Canadian government and nonprofit health and cancer organizations and searched these pages using predefined keywords to collect 246 posts. We analysed the text and audiovisuals using a codebook based on the study objectives, evidence from the literature, and constructs from the health belief model. Posts were shared between 2009 and 2020, with the highest frequency between 2011 and 2017, corresponding to several Canadian IT policy developments. Of the posts, 156 (63.4%) mentioned at least one specific consequence of IT; of these, 132 (84.6%) mentioned skin cancer. However, there were few references to other consequences of IT, such as eye and appearance damage. Additionally, only three posts recommended alternative behaviours to IT. Some evidence-based message design features, including narratives (5.3%), myth correction (26%), and normative appeals (30.5%), were less frequent. These results may help message designers leverage the large potential audience on social media to effectively address the excess cancer risk posed by IT.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".