Allocation and prioritization of hospital pharmacist clinical services in British Columbia
Bibliographic record
Abstract
Background: Clinical pharmacists are a limited resource in British Columbia (BC). Few studies have been conducted to explore hospital clinical pharmacist allocation. It is unclear how pharmacy leaders prioritize the allocation of their pharmacist staff to provide clinical services at their sites. Objectives: To characterize how hospital pharmacy leaders allocate their pharmacists within their sites. Methods: This qualitative study used key informant interviews of hospital pharmacy leaders in BC, Canada. Seven questions were included in the interview guide, asking participants about their philosophy for organizing clinical pharmacist coverage, exploring the adequacy of current staff levels, asking about barriers and enablers, and looking at the use of quality assurance mechanisms. Results: Sixteen participants were interviewed. The data yielded five themes: clinical staff allocation, barriers to providing optimal pharmacy services, clinical work prioritization, staff training and recruitment, and quality assurance. Conclusions: Pharmacy leaders in BC consider a variety of factors in allocating their clinical staff. While funding is an important factor in human resource allocation, even with adequate funding, there is a lack of adequately trained staff to fill available positions. Future exploration is needed to determine the best method of pharmacist allocation in relation to patient outcomes and to identify novel ways to support training of clinical pharmacists.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".