Pandemic H1N1 Targeted Messaging for Manitoba Metis: An Evaluation of a Risk Communication Intervention
Bibliographic record
Abstract
Certain populations are more at-risk than others during a pandemic, and health systems are required to develop targeted risk messaging to ensure that those populations have access to necessary protective materials and information. During the H1N1 pandemic in 2009–2010, the Manitoba Metis Federation (MMF), with support from Manitoba Health, carried out a door-to-door risk communication campaign that targeted particularly at-risk Metis in Manitoba, Canada. This paper is an evaluation of that campaign. To investigate Metis perceptions of the intervention, researchers conducted five focus groups (n=50 participants) with Metis citizens in two communities where targeted home visitations were carried out. To understand the rationale and intentions of the intervention, researchers also carried out key informant interviews with MMF senior staff who were responsible for developing the intervention and delivering the training to the communication messengers. Despite the positive steps taken to reach an at-risk community, the outcomes of this particular intervention ultimately did not meet its intended goals. Efforts can be made during inter-pandemic periods to build on established relationships, learn from past experiences, and develop new solutions. To ensure optimum community reception, intensive health messaging campaigns need to strategize ways to impart health expertise in ways that are culturally relevant.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".