THE DEVELOPMENT OF A PHARMACY WORKLOAD MEASUREMENT SYSTEM FOR THE CANADIAN FORCES
Bibliographic record
Abstract
Time motion and workload measurement studies have been valuable management tools since the early 1900's. They have been successfully applied to many situations and occupations to increase worker productivity and to aid managers in determining staffing levels. Pharmacy staffing levels were historically determined by intuition and various rules of thumb. By the 1960's pharmacy managers recognized the need for a more scientific method of determining proper staffing levels. The advent of clinical pharmacy services made this requirement more critical. A review of the literature shows that Rothenbuhler and Archambault, Summerfield, Hammel, Levin and Toohey used time motion studies to develop various pharmacy workload measurement systems. However, since these systems were developed in only 1 or 2 hospitals, they were difficult to implement in other pharmacy departments across the country. In Canada, Schnell conducted a study entitled, "The Development of a Canadian Hospital Pharmacy Workload Measurement System". Since his study was to provide a data base useful to all Canadian hospitals, Schnell first determined what constituted pharmacy work in Canada and then developed national standards of practice. Stopwatch time studies where then conducted in 13 hospitals representing all geographical areas of the country. The results provided the definition of 290 distinct elements of pharmacy work and the average time to complete each element. The purpose of this study was to develop a new pharmacy workload measurement system for the Canadian Forces. They presently use a line item issue system. In this study, a total of 18 workload units which describe military pharmacy work were identified. These workload units were then defined using elements which were reported in other studies or determined by direct observation of pharmacy work during on site visits to military pharmacies. The average time to complete each element was taken from the studies in which it was reported or determined by stopwatch time study during the on site visits. Adding the element times together gave the average time required to complete a workload unit. Non-measurable work was identified by having military pharmacy staff self-report daily activities and the time spent on each activity. Any activity which could not be placed under one of the defined workload units was considered non-measureable workload. In this study, non-measurable workload was 50% of measurable workload or 33% of the total departmental workload. A new workload reporting form was designed to present the new workload units and non-measurable workload. This form allowed for the easy comparison of the time spent on the various activities or category of activities. The new workload measurement system using the sum of measurable workload plus the actual non-measurable workload from the self-reporting forms was compared (by paired T -test) against the present line item issue system. The sum of measurable workload plus 50% of measurable workload was also compared to the present system. In both cases the new workload measurement system captured a significantly greater amount of productive time than the present system. This study concluded that the new workload measurement system better described the actual work being done in military pharmacy departments. It also was more efficient than the present line item issued system because it captured a greater amount of productive time over the same reporting period.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".