Digital Marketing to Promote Healthy Weight Gain Among Pregnant Women in Alberta: An Implementation Study
Bibliographic record
Abstract
BACKGROUND: As the use of digital media for health promotion has become increasingly common, descriptive studies exploring current and innovative marketing strategies can enhance the understanding of effective strategies and best practices. OBJECTIVE: This study aims to describe the implementation of a provincial digital media campaign using complementary advertising platforms to promote healthy pregnancy weight gain messages and direct a Web audience to a credible website. METHODS: The digital media campaign occurred in 3 phases, each for 8 weeks, and consisted of search engine marketing using Google AdWords and social media advertising through Facebook. All advertising materials directed users to evidence-based pregnancy-related weight gain content on the Healthy Parents, Healthy Children website. RESULTS: Google Ads received a total of 43,449 impressions, 2522 clicks, and an average click-through rate (CTR) of 5.80%. Of people who clicked on a Google ad, 78.9% (1989/2522) completed an action on the website. Across all Facebook advertisements, there were 772,263 impressions, 14,482 clicks, and an average CTR of 1.88%. The highest-performing advertisement was an image of a group of diverse pregnant women with the headline "Pregnancy weight is not the same for every woman." CONCLUSIONS: This study supports the use of digital marketing as an important avenue for delivering health messages and directing Web users to credible sources of information. The opportunity to reach large, yet targeted audiences, along with the ability to monitor and evaluate metrics to optimize activities throughout a campaign is a powerful advantage over traditional marketing tactics. Health organizations can use the results and insights of this study to help inform the design and implementation of similar Web-based activities.
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 | 0.042 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".