Creating a Culturally Safe Online Data Collection Instrument to Measure Vaccine Confidence Among Indigenous Youth: Indigenous Consensus Method
Bibliographic record
Abstract
BACKGROUND: Participating in surveys can shape the perception of participants related to the study topic. Administering a vaccine hesitancy questionnaire can have negative impacts on participants' vaccine confidence. This is particularly true for online and cross-cultural data collection because culturally safe health education to correct misinformation is typically not provided after the administration of an electronic survey. OBJECTIVE: To create a culturally safe, online, COVID-19 vaccine confidence survey for Indigenous youth designed to collect authentic, culturally relevant data of their vaccine experiences, with a low risk of contributing to further vaccine confusion among participants. METHODS: Using the Aboriginal Telehealth Knowledge Circle consensus method, a team of academics, health care providers, policy makers, and community partners reviewed COVID-19 vaccine hesitancy surveys used in public health research, analyzed potential risks, and created a framework for electronic Indigenous vaccine confidence surveys as well as survey items. RESULTS: The framework for safer online survey items is based on 2 principles, a first do-no-harm approach and applying a strengths-based lens. Relevant survey domains identified in the process include sociodemographic information, participants' connection to their community, preferred sources for health information, vaccination uptake among family members and peers, as well as personal attitudes toward vaccines. A total of 44 survey items were developed, including 5 open-ended items to improve the authenticity of the data and the analysis of the experiences of Indigenous youth. CONCLUSIONS: Using an Indigenous consensus method, we have developed an online COVID-19 vaccine confidence survey with culturally relevant domains and reduced the risk of amplifying misinformation and negative impacts on vaccine confidence among Indigenous participants. Our approach can be adapted to other online survey development in collaboration with Indigenous communities.
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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 | 0.072 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".