Developing a Competency Framework for Population Health Graduate Students Through Student and Faculty Collaboration
Bibliographic record
Abstract
Defining competencies within health disciplines is important because it provides a shared understanding of the fundamental knowledge, skills, and attitudes necessary for research and practice while also offering a practical reference point for academic preparation and professional development. However, existing literature regarding competency frameworks does not address the unique needs of interdisciplinary population health research graduate students. The purpose of this project was to understand the competencies desired by interdisciplinary population health research graduate students within the Healthy Populations Institute (HPI) at Dalhousie University and to create a competency framework on which training and program development could be based. A student-led initiative was undertaken to identify core competencies necessary for interdisciplinary population health research graduate students from both traditional (e.g., health promotion) and nontraditional health (e.g., political science) backgrounds. Data were collected and analyzed via three phases: environmental scan, community resource mapping, and consultations with HPI research scholars. Through the environmental scan, core competencies and guiding principles were identified. Community resource mapping of local employment, volunteer, educational, and/or skill-building opportunities resulted in the development of a database. Consultations confirmed the validity of competencies identified in the scan and elicited further resources and suggestions for educational and professional skill development. This project resulted in a unique competency framework that will inform ongoing program development and foster additional opportunities for graduate students within HPI. The process of creating this framework may also be of value to other universities wishing to develop or refine their own set of competencies.
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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 | 0.046 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".