Preventing Lyme disease through identification of key beliefs
Bibliographic record
Abstract
Lyme disease and other vector-borne diseases are on the rise because of climate change. In the province of Quebec, Canada, Lyme disease has become a public health problem deserving the attention of health authorities. Despite their recognized effectiveness at preventing tick-to-human transmission, rates of adoption of Lyme disease adaptive behaviours (LDAB) remain relatively low in the population. Using the Theory of Planned Behaviour (TPB), the aim of this study is to identify specific and actionable beliefs associated with the adoption of Lyme disease adaptive behaviours. Specifically, 2,011 people were surveyed to determine the decision-making process behind specific beliefs, which could be targeted for raising awareness. Statistically significant associations were found between the three determinants of the TPB (i.e., attitudes, perceived social pressure and perceived behavioral control) and the intention to adapt. In addition, the intention itself was significantly associated with adopting LDAB. Belief-based analyses indicated that 8 primary beliefs (4 behavioral beliefs, 2 normative beliefs, and 2 control beliefs) were associated with LDAB. Among these, control beliefs (barriers and facilitating factors) appeared to have the greatest impact on adaptation. These findings can be used to guide educational and awareness-raising campaigns to promote LDAB by changing or reinforcing these primary beliefs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".