Developing and Field Testing a Community Based Youth Initiative to Increase Tuberculosis Awareness in Remote Arctic Inuit Communities
Bibliographic record
Abstract
BACKGROUND: Inuit in Canada have the highest reported tuberculosis (TB) incidence rate in Canada, even higher than other Canadian Indigenous groups. The aim of this study was to increase TB awareness among Inuit youth and their communities by equipping those who can best reach this population with a community based, youth focused, education initiative built on interventions adapted from a previous TB awareness study. METHODS: The Taima TB Youth Education Initiative was a field test case study of a knowledge translation (KT) strategy aimed at community members who provide health education in these communities. In the first stage of this study, interventions from a larger TB awareness campaign were adapted to focus on youth living in remote Inuit communities. During the second stage of the study, investigators field tested the initiative in two isolated Inuit communities. It was then applied by local implementation teams in two other communities. Evaluation criteria included feasibility, acceptability, knowledge uptake and health behavior change. RESULTS: Implementation of the adapted KT interventions resulted in participation of a total of 41 youth (19 females, 22 males) with an average age of 16 years (range 12-21 years) in four different communities in Nunavut. Community celebration events were attended by 271 community members where TB messaging were presented and discussed. All of the health care workers and community members surveyed reported that the adapted interventions were acceptable and a useful way of learning to some extent. Knowledge uptake measures indicated an average TB knowledge score of 64 out of 100. Local partners in all four communities indicated that they would use the Taima TB Youth Education Initiative again to raise awareness about TB among youth in their communities. CONCLUSIONS: The TB awareness interventions adapted for the Taima TB Youth Education Initiative were acceptable to the Inuit communities involved in the study. They resulted in uptake of knowledge among participants. Implementation by local implementation teams was feasible as evidenced by the participation and attendance of youth and community members in all communities. The ability to implement the interventions by local implementation teams indicates there is potential to scale up in other remote communities in the arctic setting.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".