Factors influencing H1N1 vaccine behavior among Manitoba Metis in Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: During the first wave of the H1N1 influenza pandemic in 2009, Aboriginal populations in Canada experienced disproportionate rates of infection, particularly in the province of Manitoba. To protect those thought to be most at-risk, health authorities in Manitoba listed all Aboriginal people, including Metis, among those able to receive priority access to the novel vaccine when it first became available. Currently, no studies exist that have investigated the attitudes, influences, and vaccine behaviors among Aboriginal communities in Canada. This paper is the first to systematically connect vaccine behavior with the attitudes and beliefs that influenced Metis study participants' H1N1 vaccine decision-making. METHODS: Researchers held focus groups (n = 17) with Metis participants in urban, rural, and remote locations of Manitoba following the conclusion of the H1N1 pandemic. Participants were asked about their vaccination decisions and about the factors that influenced their decisions. Following data collection, responses were coded into the broad categories of a social-ecological model, nuanced by categories stemming from earlier research. Responses were then quantified to show the most influential factors in positively or negatively affecting the vaccine decision. RESULTS: Media reporting, the influence of peer groups, and prioritization all had positive and negative influential effects on decision making. Whether vaccinated or not, the most negatively influential factors cited by participants were a lack of knowledge about the vaccine and the pandemic as well as concerns about vaccine safety. Risk of contracting H1N1 influenza was the biggest factor in positively influencing a vaccine decision, which in many cases trumped any co-existing negative influencers. CONCLUSIONS: Metis experiences of colonialism in Canada deeply affected their perceptions of the vaccine and pandemic, a context that health systems need to take into account when planning response activities in the future. Participants felt under-informed about most aspects of the vaccine and the pandemic, and many vaccine related misconceptions and fears existed. Recommendations include leveraging doctor-patient interactions as a site for sharing vaccine-related knowledge, as well as targeted, culturally-appropriate, and empowering public information strategies to supply reliable vaccine and pandemic information to potentially at-risk Aboriginal populations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".