Graduate Attributes for Master's Programs in Health Services and Policy Research: Results of a National Consultation
Bibliographic record
Abstract
OBJECTIVE: Our objective was to identify desirable attributes to be developed through graduate training in health services and policy research (HSPR) by identifying the knowledge, skills and abilities thought to be keys to success in HSPR-related careers. We aimed for a framework clear enough to serve as a touchstone for HSPR training programs across Canada yet flexible enough to permit diversity of specialization across and within those programs. METHODS: OUR APPROACH INVOLVED SEVERAL STAGES OF DATA COLLECTION AND ANALYSIS: a review of literature; telephone interviews with opinion leaders; online surveys of HSPR students, recent graduates and employers; an invitational workshop; and an interactive panel at a national conference. Our final framework was arrived at through an iterative process of thematic analysis, reflection on invited feedback from consultation participants and triangulation with existing competency frameworks. RESULTS: Our final result was a framework that identifies traits, knowledge and abilities of master's-level graduates who are capable of fostering health system improvement through planning, management, analysis or monitoring that is informed by credible evidence and relevant theory. These attributes are organized into three levels: generic graduate attributes, knowledge related to health and health systems and, finally, attributes related to the application of knowledge for health system improvement. The HSPR-specific attributes include not only an understanding of HSPR theories and methods but also the skills related to the practical application of knowledge in the complex environments of health system decision-making and healthcare policy. CONCLUSION: Master's-level HSPR training programs should prepare students to pose and seek answers to important questions and provide them with the skills necessary to apply their knowledge within complex decision-making environments.
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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 | 0.142 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".