Population Consultation: A Powerful Means to Ensure that Health Strategies are Oriented Towards Universal Health Coverage
Bibliographic record
Abstract
BACKGROUND: We seek to highlight why population consultations need to be promoted more strongly as a powerful means to move health reforms towards Universal Health Coverage (UHC). However, despite this increasing recognition that the "population" is the key factor of successful health planning and high-quality service delivery, there has been very little systematic reflection and only limited (international) attention brought to the idea of specifically consulting the population to improve the quality and soundness of health policies and strategies and to strengthen the national health planning process and implementation. So far, research has done little to assess the significance of population consultations for the health sector and its importance for strategic planning and implementation processes; in addition, there has been insufficient evaluation of population consultations in the health sector or health-related areas. DISCUSSION: We drew on ongoing programmatic work of World Health Organization (WHO) offices worldwide, as most population consultations are not well-documented. In addition, we analyzed any existing documentation available on population consultations in health. We then elaborate on the potential benefits of bringing the population's voice into national health planning. We briefly mention the key methods used for population consultations, and we put forward recent country examples showing that population consultation is an effective way of assessing the population's needs and expectations, and should be more widely used in strategizing health. Giving the voice to the population is a means to strengthen accountability, to reinforce the commitment of policy makers, decision-makers and influencers (media, political parties, academics, etc.) to the health policy objectives of UHC, and, in the specific case of donor-dependent countries, to sensitize donors' engagement and alignment with national health strategies. CONCLUSIONS: The consequence of the current low international interest for population consultations probably has the most negative effect on resource-poor countries, as this analytical oversight comes with a high price. However, a population consultation has the potential to give more benefit and added value to contexts where resources are scarce and where planning processes pose a high extra burden, and should thus be promoted among international donor agencies.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".