Deliberative democracy in health care: current challenges and future prospects
Bibliographic record
Abstract
BACKGROUND: There is a vast body of literature on deliberative, participative, or engaged democracy. In the area of health care there is a rapidly expanding literature on deliberative democracy as embodied in various notions of public engagement, shared decision-making (SDM), patient-centered care, and patient/care provider autonomy over the past few decades. It is useful to review such literature to get a sense of the challenges and prospects of introducing deliberative democracy in health care. OBJECTIVE: This paper reviews the key literature on deliberative democracy and SDM in health care settings with a focus on identifying the main challenges of promoting this approach in health care, and recognizing its progress so far for mapping out its future prospects in the context of advanced countries. METHOD: Several databases were searched to identify the literature pertinent to the subject of this study. A total of 56 key studies in English were identified and reviewed carefully for indications and evidence of challenges and/or promising avenues of promoting deliberative democracy in health care. RESULTS: Time pressure, lack of financial motivation, entrenched professional interests, informational imbalance, practical feasibility, cost, diversity of decisions, and contextual factors are noted as the main challenges. As for the prospects, greater clarity on conception of public engagement and policy objectives, real commitment of the authorities to public input, documenting evidence of the effectiveness of public involvement, development of patient decision supports, training of health professionals in SDM, and use of multiple and flexible methods of engagement leadership suited to specific contexts are the main findings in the reviewed literature. CONCLUSION: Seeking deliberative democracy in health care is both challenging and rewarding. The challenges have been more or less identified. However, its prospects are potentially significant. Such prospects are more likely to materialize if deliberative democracy is pursued more systematically in the broader sociopolitical domains.
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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 | 0.105 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".