The Benefits and Challenges of Precepting Pharmacy Students Virtually in Interprofessional Primary Care Teams
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify pharmacists' perspectives on the benefits and challenges of precepting pharmacy students during circumstances that require using virtual care in team-based primary care practices. METHODS: A cross-sectional online survey was disseminated through Qualtrics software from July 5, 2021, to October 13, 2021. We used a convenience sampling technique to recruit a sample of pharmacists working in primary care teams across Ontario, Canada, who were able to complete a web-based survey in English. RESULTS: A total of 51 pharmacists participated in the survey and provided complete responses (response rate of 41%). Participants noted benefits at 3 levels of precepting pharmacy students in primary care during the COVID-19 pandemic: (1) benefits to pharmacists, (2) benefits to patients, and (3) benefits to students. Challenges of precepting pharmacy students were: (1) difficulty training students virtually, (2) students not being ideally prepared to begin a practicum training during a pandemic, and (3) reduced availability and new workload demands. CONCLUSION: Pharmacists in team-based primary care highlighted substantial benefits and challenges for precepting students during a pandemic. Alternative mechanisms of experiential education delivery can provide new opportunities for pharmacy care yet can also restrict immersion into interprofessional team-based primary care and diminish pharmacist capacity. Additional support and resources to facilitate capacity are critical for pharmacy students to succeed in future practice in team-based primary care.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".