Integration of clinical pharmacists into an ambulatory, pediatric hematology/oncology/transplant clinic
Bibliographic record
Abstract
OBJECTIVES: To describe key activities performed by a newly deployed clinical pharmacist in an outpatient pediatric hematology, oncology, transplant clinic. To demonstrate how utilization of the pharmacist evolved, as indicated by changes in frequency of key activities, during the first four months of integration. DESIGN: Clinical pharmacists were made consistently available in an outpatient clinic serving hematology, oncology, transplant patients and their families. A list of key activities, based on provincial clinical pharmacist standards, was created to provide a framework for the role. Over a four-month period, the pharmacists recorded the number of times activities were performed. RESULTS: Over the data collection period, obtaining best possible medication histories (203), providing medication counseling (150), and creating adherence aids (144) were the most commonly performed activities. In comparison to the first month, key activities increased by 73% in the fourth month. Notably, providing recommendations for drug therapy (156%), assessments of adherence (122%), and best possible medication history collection (88%) increased considerably. CONCLUSIONS: The integration of a pharmacist into an outpatient pediatric hematology, oncology, transplant clinic resulted in the provision of several key clinical pharmacy services. As the role developed, activities were performed more frequently, demonstrating growth in utilization of the pharmacist.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".