COVID the Catalyst for Evolving Professional Role Identity? A Scoping Review of Global Pharmacists’ Roles and Services as a Response to the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic requires a range of healthcare services to meet the needs of society. The objective was to explore what is known about the roles and services performed by frontline pharmacists during the first year of the COVID-19 pandemic. A scoping review was conducted of frontline pharmacists' roles and services during the first year of the COVID-19 pandemic. A medical librarian conducted comprehensive searches in five bibliographic databases-MEDLINE (via Ovid), Embase (Ovid), CINAHL, Scopus, and Web of Science Core Collection for articles published between December 2019 and December 2020. The initial search retrieved 3269 articles. After removing duplicates, 1196 articles titles and abstracts were screened, 281 full texts were reviewed for eligibility, and 63 articles were included. This scoping review presents a conceptual framework model of the different layers made visible by COVID-19 of pharmacist roles in public health, information, and medication management. It is theorized that there is an invisible layer of change representing evolving professional role identity that may influence permanent role change following the pandemic. Thus, the pharmacy profession needs to build upon the lessons and experiences of this global pandemic and not let the momentum of the visible and invisible changes go to waste.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".