Development of Enriched Core Competencies for Health Services and Policy Research
Bibliographic record
Abstract
OBJECTIVE: To develop an enriched set of core competencies for health services and policy research (HSPR) doctoral training that will help graduates maximize their impact across a range of academic and nonacademic work environments and roles. DATA SOURCES/STUDY SETTING: Data were obtained from multiple sources, including literature reviews, key informant interviews, stakeholder consultations, and Expert Working Group (EWG) meetings between January 2015 and March 2016. The study setting is Canada. STUDY DESIGN: The study used qualitative methods and an iterative development process with significant stakeholder engagement throughout. DATA COLLECTION/EXTRACTION METHODS: The literature reviews, key informant interviews, existing data on graduate career trajectories, and EWG deliberations informed the identification of career profiles for HSPR graduates and the competencies required to succeed in these roles. Stakeholder consultations were held to vet, refine, and validate the competencies. PRINCIPAL FINDINGS: The EWG reached consensus on six sectors and eight primary roles in which HSPR doctoral graduates can bring value to employers and the health system. Additionally, 10 core competencies were identified that should be included or further emphasized in the training of HSPR doctoral students to increase their preparedness and potential for impact in a variety of roles within and outside of traditional academic workplaces. CONCLUSION: The results offer an expanded view of potential career paths for HSPR doctoral graduates and provide recommendations for an expanded set of core competencies that will better equip graduates to maximize their impact on the health system.
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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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".