Managing multiple projects: a literature review of setting priorities and a pilot survey of healthcare researchers in an academic setting.
Bibliographic record
Abstract
OBJECTIVES: To summarize and then assess with a pilot study the use of published best practice recommendations for priority setting during management of multiple healthcare research projects, in a resource-constrained environment. METHODS: Medical, economic, business, and operations literature was reviewed to summarize and develop a survey to assess best practices for managing multiple projects. Fifteen senior healthcare research project managers, directors, and faculty at an urban academic institution were surveyed to determine most commonly used priority rules, ranking of rules, characteristics of their projects, and availability of resources. Survey results were compared to literature recommendations to determine use of best practices. RESULTS: Seven priority-setting rules were identified for managing multiple projects. Recommendations on assigning priorities by project characteristics are presented. In the pilot study, a large majority of survey respondents follow best practice recommendations identified in the research literature. However, priority rules such as Most Total Successors (MTS) and Resource Scheduling Method (RSM) were used "very often" by half of the respondents when better performing priority rules were available. CONCLUSIONS: Through experience, project managers learn to manage multiple projects under resource constraints. Best practice literature can assist project managers in priority setting by recommending the most appropriate priority given resource constraints and project characteristics. There is room for improvement in managing multiple projects.
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How this classification was reachedexpand
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 | 0.039 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".