Research methodology: tracking the role of medical doctors in healthcare reforms
Bibliographic record
Abstract
The objective of empirically exploring the role of medical doctors in healthcare reforms and policy changes raises a number of methodological questions. What data set should be considered? What is the appropriate period of study (that is, when should analysis of reforms start and end our)? What context-specific elements, whether jurisdictional or situational, influence agency in healthcare reforms? What characterises the roles played by various actors in the reform process? With what influence on context and policy outcomes? What methods should be used to compare case studies? These questions led us to consider methodological developments in contextualist and process research (Mintzberg and Waters, 1982; Pettigrew, 1987, 2012; Langley, 1999), which appear as a plausible way to approach policy research. We thus look at policy changes, such as healthcare reforms, as a continuing system in becoming (Pettigrew, 1987; Tsoukas and Chia, 2002). We rely on comparative longitudinal case studies (Fitzgerald and Dopson, 2009) to track the evolving dynamics of healthcare reforms and medical politics in two national empirical contexts: the NHS in England and the healthcare systems of two Canadian provinces: Quebec and Ontario. A number of logical arguments support the selection of these two national jurisdictions for our research. Both have a tax-based PFHS. Both have been fertile ground for healthcare reforms and are frequently selected as case studies in comparative health policy analysis (Tuohy, 1999, 2018).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".