The Career Outcomes of Health Services and Policy Research Doctoral Graduates
Bibliographic record
Abstract
OBJECTIVE: To examine the career outcomes of 20 years of PhD graduates from Canadian health services and policy research (HSPR) doctoral training programs. METHODS: The deans of the doctoral training programs were invited to participate in this national cohort study. A standardized career-tracking template was developed. Internet searches of publicly accessible sources were used to track graduates' employment. Descriptive analyses summarized PhD program characteristics and current employment. RESULTS: Of the 1,208 trainees who graduated during our study period, 884 (73.2% of 1,208, or 90.3% of the 979 with complete data) could be successfully tracked. HSPR PhD graduates are highly employable, but employment trends have changed over time. Today's graduates are more likely to enter careers in a wider variety of sectors and roles and are less likely to be employed in academia than previous graduates. However, over 50% of graduates are currently employed in professorial positions within the academic sector or in research roles or departments within healthcare delivery organizations. CONCLUSIONS: This article provides an initial descriptive profile of the career outcomes of HSPR PhD graduates in Canada from 10 university-based doctoral training programs. To ensure that PhD graduates are prepared to contribute fully within diverse sectors and roles, doctoral training must evolve to keep pace with employment trends and encompass, in addition to research skills, the professional skills demanded in the public, private, not-for-profit and healthcare delivery sectors.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".