Introduction: Medical doctors and healthcare reforms
Bibliographic record
Abstract
This Introduction defines our research objectives and the key concepts underpinning our inquiry. We look at reforms in contemporary welfare states, which include England and Canada (Denhardt and Denhardt, 2000; Bejerot and Hasselbladh, 2011; Ferlie and McGivern, 2013) and on their implications for the potential roles and manifestations of the agency of medical doctors (Denis et al, 2016). Setting the scene: reforms in contemporary healthcare systems The question of healthcare reforms has attracted growing interest among policy analysts and health researchers (Greener, 2009; Ham, 2009; Lazar et al, 2013; Tuohy 2018; Germain, 2019). Reform is a privileged mode of intervention used by liberal democracies to intervene in various policy areas (Rocher, 2008). In their comparative analysis of public management reforms, Pollitt and Bouckaert (2017) define reforms as ‘deliberate changes to the structures and processes of a system with the objective of getting them (in some sense) to run better’ (Pollitt and Bouckaert, 2017: 2). In the healthcare context, this means improving patient experience, healthcare professionals’ satisfaction with work, population health and long-term system viability. Pollitt and Bouckaert's analysis suggests that reform is embedded in a complex web of institutional arrangements and political processes that shape the destiny of reformative ideas and reformers (Marmor and Wendt, 2012; Tuohy, 2018; van Gestel et al, 2018). As suggested by Mechanic and Rochefort (1996), comparable healthcare systems of various nations face similar challenges but their responses vary according to national context and institutions. Reforms tend to unfold according to sedimentation logic where previous structures, positions and views re-emerge to frame current ambitions and scope for change (Pollitt and Bouckaert, 2017). Timing is central to the process and reinforces the importance of context in shaping the destiny of reforms (van Gestel et al, 2018). In addition, insufficient capacity to resolve persisting issues creates a propensity in some health systems, including in England and Canada, to embark in cyclical reforms (Greener, 2009; Ham, 2009; Forest and Martin, 2018; Germain, 2019). Escalating healthcare costs and technological breakthroughs in drug development, artificial intelligence (AI) and digital health suggest that systems will face increasing challenges to design, deploy and renew policy instruments. Healthcare reforms, with their trail of destabilisation and reorganisation, are a permanent feature of welfare states (Klein, 2018).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".