Training workload in the investigational drug service of a university hospital center
Bibliographic record
Abstract
PURPOSE: Training represents a considerable portion of research activities and is vastly different for each clinical trial. This variation is partially explained by the lack of detailed regulations surrounding training procedures, which hinders the ability of investigational drug service (IDS) staff to plan their workload. The aim of this study was to quantify the workload associated with trial-specific training of IDS staff. The secondary aim was to identify the factors associated with training complexity. METHODS: A retrospective study was carried out in the IDS of a mother and child university hospital. Trial-specific documents on which the pharmacy staff was trained were analyzed. Workload was calculated by measuring reading time. The readability of each document was determined by the Flesch Reading Ease score. The complexity of the trials was established using the scoring method of Calvin-Lamas et al. The influence of the following factors on training was assessed by analysis of variance: sponsor type, research phase, and research focus by medical specialty. RESULTS: A total of 93 clinical trials and 433 documents were included. Investigator's brochures were the longest (a mean [SD] of 107 [46] pages; P < 0.0001) and most difficult documents to read (mean [SD] readability score, 25.5 [4.4]; P < 0.0001). Trials with industry sponsors required a significantly longer overall reading time (mean [SD], 12.26 [6.72] hours; P < 0.0001). On average, a mean (SD) of 9.42 (7.16) hours of reading were necessary to train one employee for a clinical trial. CONCLUSION: This study is the first to document reading time necessary for training of IDS staff. The training workload varied by sponsor type, while the research phase and medical specialty had little impact. IDS units would benefit from a tool that could identify complex trials.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".