Factors Leading Municipal Authorities to Implement Preventive Interventions for Lyme Disease
Bibliographic record
Abstract
The aim of this study is to document climate change adaptation interventions targeting Lyme disease at the municipal level in the province of Quebec (Canada). This exploratory study relies on the theory of planned behavior and certain constructs from the health belief model to identify the factors leading municipal authorities to implement preventive interventions for Lyme disease (PILD). Data were obtained from an online survey sent, during the summer of 2018, to municipal officers in 820 municipalities in Quebec, in all health regions where the population is at risk of contracting Lyme disease (response rate = 36%). The questionnaire was used to measure the implementation of PILD, the intention to implement these interventions, attitudes, perceived social pressure, perceived control (levers and barriers) over interventions, perceived effectiveness of preventive measures, risk, and perceived vulnerability. Results of structural equation analyses showed that attitudes were significantly associated with municipal authorities' intention to implement PILD, while the intention to implement PILD was a significant predictor of the implementation of PILD. Additional analyses showed that perceived barriers added a moderating effect in the intention-implementation relationship. The prediction of behaviors or practices that municipal authorities could implement to prevent Lyme disease will enable the evaluation over time of the evolution of Quebec municipalities' adaptation to Lyme disease. Moreover, the examination of the associations of specific psychosocial factors revealed important implications for the design of effective behavior-change interventions, which would allow health officials doing awareness work to create personalized interventions better suited to municipal officers and their specific contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".