Health decentralisation
Bibliographic record
Abstract
The decentralisation of health competences in Portugal was launched with the aim of bringing decision-making closer to local realities and strengthening municipalities’ role in promoting population well-being. A regulatory framework introduced between 2018-2019 enabled the transfer of specific responsibilities from central government to local governments. However, by 2022, only around a quarter of eligible municipalities had accepted these competences, highlighting the existence of structural, political, and financial barriers to reform. While the effects of decentralisation on health system have been widely studied, there remains limited empirical evidence on the factors that influence local jurisdictions’ willingness to assume new responsibilities. Addressing this gap, the present study analyses 201 eligible Portuguese mainland municipalities over the 2020-2022 period, modelling acceptance decisions based on demographic, political, financial, and health-related variables through binary logistic regression. Findings reveal that acceptance was more likely in municipalities politically aligned with the central government, with greater per capita financial resources, and with younger population profiles. In addition, regional dynamics emerged as an important contextual factor. These results highlight the need for decentralisation processes to account for territorial diversity, funding adequacy, and local capacity-building in order to ensure equitable and effective implementation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".