Training for Impact: PhD Modernization as a Key Resource for Learning Health Systems
Bibliographic record
Abstract
The Institute of Medicine (IOM) has articulated a vision of a learning health system (LHS) as one that provides the best care at lower costs and that constantly, systematically and seamlessly improves based on data and evidence (IOM 2013). The IOM identifies the four foundational characteristics of an LHS as the real-time use of data and informatics to capture the care experience, patient-clinician partnerships, incentives aligned for value and a leadership-instilled culture of learning (IOM 2013). Although much policy research and commentary has focused on informatics and incentives, relatively less has focused on the critical question of creating a culture of learning in these systems. And although its source is debated, most management gurus agree with the adage that "culture eats strategy for breakfast" (Cave 2017), which is why a focus on the cultural dimension is critically important. Some scholars have recognized the important role of human capital - and of front-line clinicians in particular - in the LHS (Verma and Bhatia 2016). In addition to clinicians, doctorally prepared individuals, such as those with a PhD in health services and policy research (HSPR) and fields such as health economics, epidemiology and health informatics, have the potential to make significant contributions to LHSs and health system reform (Bornstein 2016; Brown and Nuti 2016; CIHR-IHSPR 2016). But having a PhD in these fields is not the same as being prepared to support progress toward an LHS. As argued in other papers, substantial change in doctoral training is needed so that graduates can contribute to their full potential and help drive real innovation within the health system (Bornstein 2016; CIHR-IHSPR 2016; Reid 2016).
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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 | 0.039 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".