Strategic Social Marketing in Canada: Ten Phases to Planning and Implementing Cancer Prevention and Cancer Screening Campaigns
Bibliographic record
Abstract
Social marketing has gained prominence among researchers and practitioners working in social issues. Campaigns labelled as social marketing are implemented worldwide by governments, nonprofit organizations, and private industry. However, there is evidence that the concepts and processes associated with these campaigns are misunderstood and not consistently applied. This article describes social marketing benchmark criteria and follows with an overview of a strategic process for social marketing, specifically: planning, research, campaign development, implementation, monitoring, and evaluation. The main focus is the social marketing development process undertaken by the Alberta Health Services to plan and implement cancer prevention and cancer screening campaigns. Unique to this process is an internationally recognized panel of social marketing experts to review and provide consultation. Improvements in planning and implementing campaigns are outlined, including stakeholder engagement, use of audience-centred strategies, and endorsement of campaigns by management. The primary outcome of bringing together a clear and organized social marketing tool for cancer prevention and cancer screening in Alberta is the development, implementation, and evaluation of campaigns that follow a systematic best-practice process. The learnings from this process will have important implications provincially in Alberta, nationally in Canada, and worldwide for the advancement of the social marketing discipline.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".