Pharmacy Travel Health Services in Canada: Experience of Early Adopters
Bibliographic record
Abstract
Since 2007, community pharmacists in Canada have become increasingly involved in delivering Travel Health services, including the recommendation and administration of vaccines. This qualitative scoping survey examines some of the activities and opinions of those early pharmacist adopters delivering these services. A Survey Monkey free text questionnaire was emailed to pharmacists who were involved in delivering travel medicine services. 21 pharmacists responding represented seven Canadian provinces. Only 5 pharmacists estimated that they were seeing five or more patients a week on average. Amongst the challenges they faced the most quoted was lack of time when running a busy pharmacy (62%) a lack of prescribing authority, (52%), and lack of access to public health vaccines (52%). 'Word of mouth' was widely quoted as a means of developing the service, indicating a good patient satisfaction. Also expressed were the advantages of convenience in terms of being a 'one stop shop', ease of billing to insurance companies and convenient appointment times. There are a number of challenges which are still to be faced which may be resolved by further legislation allowing access to public health vaccines and more widespread prescribing rights. The relatively low level of consultations reported by some is of concern if those pharmacists are to maintain competence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".