Assessing Palliative Care Development in Mexico Through the WHO Actionable Indicators Model
Bibliographic record
Abstract
CONTEXT: Assessing the development of palliative care (PC) is essential to advancing PC delivery worldwide. The World Health Organization (WHO) offers a new conceptual model for assessing PC development that focuses on identifying gaps in service provision, which helps define priorities and guide decisions. Previous reports ranked Mexico at a high level of PC development, described as an early integration stage in the health system. However, this updated framework offers a more profound and holistic analysis by providing previously unavailable data. OBJECTIVES: To document the current state of PC development in Mexico through the WHO actionable indicators model. METHODS: Six components were measured: a) PC provision; b) use of essential medicines; c) education and training; d) research; e) health policies; and f) empowerment of people and communities. Fourteen indicators were individually ranked into four levels of development: 1) emerging, 2) intermediate, 3) established, and 4) advanced. RESULTS: Mexico's PC development has focused on specialization streams for physicians, increased awareness through scientific conferences, publications, and community organizations, and the inclusion of PC in the national basic health package and national and local laws. In contrast, PC development is halted by insufficient PC services, a lack of a national PC authority, association, or plan, limited access to essential medications, and scarce undergraduate education. CONCLUSIONS: This study shows a regression in the level of PC development in Mexico through an updated analysis, highlighting the gaps that need to be urgently addressed. These findings help continue PC advocacy, growth, and implementation in the region.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".