Medication management surrounding transitions of care: A qualitative assessment of community pharmacists’ preferences (MEMO TOC)
Bibliographic record
Abstract
BACKGROUND: Multiple medication changes during hospitalization increase the risk of errors upon discharge. Community pharmacists may face barriers to providing pharmaceutical care because of the lack of clinical information and communication from hospitals. Studies implementing handover to community pharmacists upon hospital discharge reported improved patient outcomes, but interventions were time-consuming. METHODS: One-on-one interviews and a focus group were conducted to identify community pharmacists' barriers to providing care to patients recently discharged from hospital and to determine their preferences for hospital discharge prescriptions. Transcripts were qualitatively analyzed using an inductive semantic approach. RESULTS: Four one-on-one interviews and an 8-participant focus group were conducted. Participants described barriers to providing care to discharged patients, including lack of communication, incomplete prescriptions, and limited clinical information. Participants identified that the most valuable information to include comprised laboratory values, hospital contact information and annotation of medication changes. These items would improve their abilities to provide timely and high-quality pharmaceutical care. INTERPRETATION: Our results were similar to prior literature identifying a lack of communication and clinical information as barriers to providing care to recently discharged patients. Unexpectedly, study participants did not rate medication indication as a strongly preferred information item. CONCLUSIONS: 2020;153:xx-xx.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".