Development of Research Core Competencies for Academic Practice Among Health Professionals: A Mixed-Methods Approach
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Of the 4 pillars of academic practice for nursing and allied health, research has been the least developed and no standard competency framework exists that is embedded in health professional scopes of practice. The objective of this article is to report on the preliminary development and pilot-testing of research and academic scholarship core competencies for nonphysician health professionals working within a large urban academic health sciences center. METHODS: We conducted an internal and external environmental scan and multiphase consultation process to develop research and academic core competencies for health professionals working within an interprofessional setting. RESULTS: The final framework outlines 3 levels of research proficiency (novice, proficient, and advanced) and the relevant roles, specific competencies, and observable actions and/or activities for each proficiency level. CONCLUSIONS: Organizations should consider the integration of the framework within performance management processes and the development of a road map and self-assessment survey to track progress over time and support health professionals with their academic practice goals.
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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 | 0.100 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".