Physician perspectives on collaborative working relationships with team-based hospital pharmacists in the inpatient medicine setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Collaborative care between physicians and pharmacists has the potential to improve the process of care and patient outcomes. Our objective was to determine whether team-based pharmacist care was associated with higher physician-rated collaborative working relationship scores than usual ward-based pharmacist care at the end of the COLLABORATE study, a 1 year, multicentre, controlled clinical trial, which associated pharmacist intervention with improved medication use and reduced hospital readmission rates. METHODS: We conducted a cross-sectional survey of all team-based and usual care physicians (attending physicians and medical residents) who worked on the participating clinical teaching unit or primary healthcare teams during the study period. They were invited to complete an online version of the validated Physician-Pharmacist Collaboration Index (PPCI) survey at the end of the study. The main endpoint of interest was the mean total PPCI score. KEY FINDINGS: Only three (response rate 2%) of the usual care physicians responded and this prevented us from conducting pre-specified comparisons. A total of 23 team-based physicians completed the survey (36%) and reported a mean total PPCI score of 81.6 ± 8.6 out of a total of 92. Mean domain scores were highest for relationship initiation (14.0 ± 1.4 out of 15), and trustworthiness (38.9 ± 3.7 out of 42), followed by role specification (28.7 ± 4.3 out of 35). CONCLUSION: Pharmacists who are pursuing collaborative practice in inpatient settings may find the PPCI to be a meaningful tool to gauge the extent of collaborative working relationships with physician team members.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it