Improving access to post-exposure prophylaxis for Lyme disease: a role for community pharmacists
Bibliographic record
Abstract
Abstract Issue Virtually non-existent in Eastern Townships (ET) before 2010, Lyme disease incidence rate reached 52 confirmed cases per 100 000 in some districts in 2018, leading to post-exposure prophylaxis (PEP) recommendation by public health. To improve access to PEP, community pharmacists can now screen and initiate PEP under a collaborative practice agreement (CPA) with the regional Medical Officer of Health. Knowledge, attitudes and practices of pharmacists regarding Lyme disease PEP were surveyed after implementation of this measure. Description of the problem 312 community pharmacists practicing in ET were invited, in October 2018, to complete an online survey, based on Godin’s integrative model (2012), with questions on professional characteristics, knowledge of CPA, attitudes about PEP, and facilitators and barriers to the use of CPA. Pharmacist’s practices were evaluated using 8 clinical vignettes. Vignette-specific and a global score were calculated. Bivariate analyses were done to test the association between global score and knowledge, attitudes, facilitators and barriers. Results Response rate was 13.8%. Most pharmacists knew (97.4%) and were in favor (93.1%) of the CPA, and believed it was effective to prevent Lyme disease (96.4%). The main barriers reported to using the CPA were related to the assessment of PEP criteria. With regards to practice, pharmacists answered correctly for clients presenting all criteria for PEP (80.6%), aged < 8 y.o. (51.6%), who had their tick removed for >72 hrs (67.7%), already presenting with symptoms (32.3%) or who had exposure in a non-endemic area (38.7%). Finally, none of the variables studied were associated with the global score. Lessons This measure has now been expanded in other endemic regions in Quebec and a provincial CPA is under development. These results will help guide the development of the provincial CPA, specifically to better consider and to provide guidance regarding PEP contraindications. Key messages Pharmacist-initiated PEP is an innovative approach to increase timely population access to an essential preventive measure in the fight against Lyme disease in endemic districts. Additional guidance is required for pharmacists with regards to assessment of PEP criteria in future CPAs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".