Policy-making: Polarization and interest groups influence
Bibliographic record
Abstract
Background Public policies have a direct impact on most determinants of health. As such, there is an ever-growing interest in the field to understand policy-making processes and determinants. Conceptually, public health relevant models on policy-making range from very rational and instrumental to mostly political and power-based. Objective: We conducted a sequential mixed method project to gain a deeper understanding of policy-making processes related to improving healthcare system performance in Quebec (Canada). We explored the hypothesis that inter-group divergences in policy preferences explained the observed incapacity to implement programmatically sound policy solutions to pervasive performance problems. Methods The research design was an exploratory sequential design divided into two phases: in-depth interviews with key stakeholder representatives in the healthcare system, followed by an open question survey among different groups of administrators and professionals, physicians, nurses, and pharmacists. Data were analyzed narratively and graphically using an innovative method derived from social network analysis and graph theory. Results Rather than divergence, the results showed striking intergroup convergence around what appeared to be a programmatically sound policy package aimed at strengthening primary care delivery capacities. Conclusions These results are interpreted in light of elitist political science perspectives on the policy process. They suggest that the incapacity to reform the system might be explained by one or two influential interest groups’ having a de facto veto in policy-making. Key messages: Our results are convergent with strongly elitist conceptions of the policy-making process wherein political clout and power are the main determinants of policy content. We hypothesize that the social psychology concept of pluralistic ignorance could help explain those groups’ monopolization of power.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".