Universal healthcare coverage, patients' rights, and nurse-patient communication: a critical review of the evidence
Bibliographic record
Abstract
The Sustainable Development Goals adopted by world leaders on September 25, 2015, aimed to end poverty and hunger, promote gender equity, empower women and girls, and ensure human dignity and equality by all human beings in a healthy environment. These development goals were premised on international human rights norms and institutions, thereby acknowledging the relevance of human rights in achieving each goal. Particularly, sustainable development goal 3, whose objective is to achieve universal health coverage, enhance healthy lives, and promote well-being for all, implicitly recognizes the right to health as crucial. Our focus in this paper is to discuss how promoting patients' rights and enhancing effective nurse-patient communication in the healthcare setting is a significant and necessary way to achieve universal health coverage. Through a critical review of the empirical research evidence, we demonstrated that enhancing patients' rights and effect nurse-patient communication will promote people-centered care, improve patients' satisfaction of care outcomes, increase utilization of care services, and empower individuals and families to self-advocate for their health. These steps directly impact primary healthcare strategies and the social determinants of health as core components to achieving universal health coverage. We argue that without paying attention to the human rights dimensions or employing human rights strategies, implementing the other efforts will be inadequate and unsustainable in protecting the poorest and most vulnerable populations in the achievement of goal 3.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".