Development and Testing of a Clinical Practice Framework for Pharmacists to Assess Patients’ Travel-Related Risks: The 5W Approach to Travel Risk Identification
Bibliographic record
Abstract
Objective: To assist with identifying patients who may be managed by pharmacists without additional travel medicine training, versus those who may benefit from referral, we developed and validated a clinical practice framework. This framework was then piloted in eight pharmacies in Ontario, Canada, from March to August 2019. Methods: A panel of experts, comprised of physicians and pharmacists from Ontario, Canada, holding a Certificate in Travel HealthTM from the International Society of Travel Medicine was recruited. This panel participated electronically in the development of the framework in three stages: (1) Sharing their current approach when performing information gathering and assessing risk in a traveling patient; (2) judging of items collated from all panellists on the basis of how essential they are to a risk assessment; and (3) validation of items deemed essential by the panel using the Item and Average Content Validity Index. The framework was then released to community pharmacies, where pharmacists that self-identified as beginners to travel medicine completed pre- and post-test phase surveys to determine the utility of the framework. Key Findings: A total of 64 items for consideration were deemed essential enough to proceed to content validation, organized into 5 ‘W’ domains: Who, What, Where, When, and Why. Each item was ranked by the experts according to its relevancy, resulting in an Average-Content Validity Index of 0.91. The resulting framework was titled “The 5W Approach to Travel Risk Identification.” This clinical practice framework is the first published assessment tool for travel medicine tailored for pharmacy’s scope of practice that has been content validated. Pharmacists reported that the framework is simple to use and provides structure for interactions with travelling patients. However, it may not be as beneficial for those with a higher level of travel medicine expertise than the average pharmacist. Conclusion: The 5W Approach tool allows pharmacists inexperienced in travel medicine to collect information when required to use their professional judgement when assessing traveling patients as either high-risk (requiring a referral to a travel medicine specialist) or low-risk. With the aim of supporting pharmacists to be more confident in caring for traveling patients and increasing their involvement in travel medicine, future research will test this framework for feasibility in Canadian community pharmacy practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".