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. Selection of the case studies in England and Canada 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). While England's PFHS predates those in Canada, they each span some 50 years, providing ample material for the study of healthcare reforms. They are both considered mature healthcare states (Tuohy, 2012) with free access to healthcare at the point of use, universal access and coverage, and the right to equality of treatment. Both jurisdictions also have a well-paid and well-trained medical profession, and medical doctors are considered key players and privileged interlocutors in reforms (Klein, 1990; Lazar et al, 2013). The healthcare systems in these two jurisdictions are highly regulated and centrally governed, even though medical doctors remain largely self-regulated. Inboth jurisdictions (although not exclusively so in England), medical doctors negotiate their participation in the system through organised and recognised bodies such as unions and professional associations.
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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 | 0.123 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".