<p>The Impact of a Training Program on Clinical Pharmacists on Pharmacy Clinical Services in a Tertiary Hospital in Hunan China</p>
Bibliographic record
Abstract
BACKGROUND: Prior to 2015, clinical consultation was the only clinical service provided by clinical pharmacists in Changsha Second Hospital. Between 2015 and 2017, a train-the-trainer program was implemented to train clinical pharmacists to provide pharmaceutical care and to conduct clinical research. The objective of the study is to examine the impact on the clinical services provided by pharmacists after the implementation of the train-the-trainer program. PATIENTS AND METHODS: Between 2004 and 2014, all completed clinical consultation activities were tallied and summarized. The results from the tallied consultation activities were used as a baseline for clinical activities provided by pharmacists prior to the training. A structured training program was implemented between 2015 and 2017 to train clinical pharmacists to provide pharmaceutical care. After the implementation of the training program was completed, all clinical activities provided by pharmacists between January 2017 and December 2017 were documented in the clinical workload form. The clinical activities completed by each pharmacist were tallied and summarized. RESULTS: Between 2004 and 2014, a total of 6569 (average 657 per year) pharmacy consultations were requested and completed from a total of 44 departments. In 2017, a total of 15,078 hrs of clinical activities were logged. The pharmacists completed 3481 consultations in 2017 (an increase of 430%), averaging 316 consultations for each pharmacist and 271.8 hr per pharmacist. Over 2000 hrs (of the 15,078 hrs) were spent on direct patient care by the pharmacists. CONCLUSION: This study shows that there was a 430% increase in clinical pharmacy consultation services provided by the clinical pharmacists after the implementation of the training program. This is directly related to the number of well-trained pharmacists available. After the implementation of the train-the-trainer program, the range of services as well as the number of clinical services and clinical hours spent on providing pharmaceutical care have significantly increased.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".