Benefits, challenges and contributors to the introduction of new hospital-based outpatient clinic pharmacist positions
Bibliographic record
Abstract
Background: Pharmacists working within interprofessional teams in the outpatient setting are well placed to address medication-related problems before and after hospital admission. Therefore, exploration of these roles is warranted. Objectives: To explore pharmacists' and other health professionals' perspectives of the impact of pharmacists working within interprofessional teams in outpatient clinics. Furthermore, we endeavoured to identify both the challenges and contributors to success with the introduction of pharmacists into these settings. Methods: This qualitative study involved semi-structured interviews with both hospital outpatient clinic pharmacists and other clinic health professionals to gain an in-depth understanding of how the introduction of pharmacists into clinics impacted clinic processes, patient care, and relationships with other health professionals. Participants were recruited from the outpatient clinics who had recently added a pharmacist to their service. Participants involved in setting up the roles were invited to participate in a voluntary interview, the transcripts from which were analysed into themes and sub-themes using an inductive and deductive approach. Results: A total of 34 staff were interviewed of which 68% were female and 74% were aged between 31 and 50 years. The cohort included 16 outpatient pharmacists, nine pharmacist team leaders, five clinic nurses and four clinic doctors (specialist consultant or registrar). Three overall themes were identified: the benefits, the contributors, and the challenges of introducing clinical pharmacy services to outpatient clinics. When establishing a clinic role, pharmacists' awareness, adaptability, and strong communication were shown to be key traits to building rapport and trustworthiness with the established clinic team. Conclusions: When pharmacists are integrated into multidisciplinary outpatient clinics they and their colleagues believe that they provide benefits to the patients and the clinics. Decision makers need to be cognizant of factors that contribute to, as well as those that impede, the successful implementation of outpatient pharmacist roles.
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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".