A before and after cross-sectional analysis of a public health campaign to increase kidney health awareness in a Canadian province
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) has a major impact on patient health and health system resources. The prevalence of kidney disease is increasing, with Manitoba being one of the provinces in Canada with the highest per capita rate of CKD and end stage renal disease (Anonymous, Canadian organ replacement register annual report: treatment of end-stage organ failure in Canada, 2001-2010, 2011). In 2011, a public health campaign to promote kidney health, by increasing awareness of CKD and its risk factors, was created to target high-risk individuals such as First Nations and those with hypertension and diabetes in urban and rural/remote Manitoba. In this study, we aimed to determine the effectiveness of this public health campaign on increasing the awareness of CKD. METHODS: Our public health campaign ran in March 2011, and employed a multifaceted approach with radio, television, internet, and print advertisements. Campaign awareness and understanding of the public health message were assessed with a telephone omnibus survey of randomly selected individuals with a Manitoba area code during February and April 2011. A before and after cross-sectional analysis was utilized to measure the effect of exposure to the campaign in telephone respondents. RESULTS: 1606 individuals participated in the survey (804 pre and 802 post). Overall awareness of the campaign messaging increased from 7% pre campaign to 25% in the post campaign period. Approximately two-thirds of respondents correctly identified a main theme message of the campaign. Awareness improved across most subgroups surveyed aside from those with lower education and income. CONCLUSIONS: Our study demonstrates the effective reach of our campaign and its relative effectiveness at raising awareness of chronic kidney disease and its risk factors.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".