Competency indices to assess the knowledge, skills and abilities of clinical research professionals
Bibliographic record
Abstract
<p class="abstract"><strong>Background:</strong> Clinical research in the 21<sup>st</sup> century will require a well-trained workforce to ensure that research protocols yield valid and reliable results. Several organizations have developed lists of core competencies for clinical trial coordinators, administrators, monitors, data management/informaticians, regulatory affairs personnel and others.</p><p class="abstract"><strong>Methods:</strong> We used data collected by the joint task force on the harmonization of core competencies from a survey of research professionals working in the US and Canada to create competency Indices for clinical research professionals. Respondents reported how competent they believed themselves to be on 51 clinical research core competencies.</p><p class="abstract"><strong>Results:</strong> Factor analyzes identified 20 core competencies that defined a competency index for clinical research professionals—general (CICRP-General, i.e., GCPs) and four sub-indices that define specialized research functions: Medicines Development; Ethics and Participant Safety; Data Management; and Research Concepts. </p><p><strong>Conclusions:</strong> These indices can be used to gage an individual’s readiness to perform general as well as more advanced research functions; to assess the education and training needs of research workers; and to evaluate the impact of education and training programs on the competency of research coordinators, monitors and other clinical research team members.</p>
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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 | 0.211 | 0.795 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".