Collaboration between pharmacists, physicians and nurse practitioners: A qualitative investigation of working relationships in the inpatient medical setting
Bibliographic record
Abstract
While collaborative, team-based care has the potential to improve medication use and reduce adverse drug events and cost, less attention is paid to understanding the processes of well functioning teams. This paper presents the findings from key informant interviews and reflective journaling from pharmacists, physicians and nurse practitioners participating in a multicentre, controlled clinical trial of team-based pharmacist care in hospitalized medical patients. A phenomenological approach guided the data analysis and content analysis was the primary tool for unitizing, categorizing and identifying emerging themes. Pharmacists experienced highs (developing trusting relationships and making positive contributions to patient care) and lows (struggling with documentation and workload) during integration into the medical care team. From the perspective of the participating pharmacists, nurse practitioners and physicians, the integration of pharmacists into the teams was felt to have facilitated positive patient outcomes by improving team drug-therapy decision-making, continuity of care and patient safety. Additionally, the study increased the awareness of all team members' potential roles so that pharmacists, nurses and physicians could play a part in and benefit from working together as a team. Focussed attention on how practice is structured, team process and ongoing support would enable successful implementation of team-based care in a larger context. (ClinicalTrials.gov number, NCT00351676).
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".