The capability approach: a guiding framework to improve population health and the attainment of the Sustainable Developmental Goals
Bibliographic record
Abstract
The world is progressing in terms of communication, innovative technology and cure of various diseases through advanced pharmacological preparations. Unfortunately, populations are still struggling with ill-health, disabilities, poverty, hunger, inequality, gender disparities and conflicts. Several questions come to mind in this regard: why are prosperity, health, peace and progress not evenly distributed and what is the best approach to address the issues associated with population health? The capability approach may offer a possible model. This approach is a blend of 5 key concepts: capabilities, functioning, agency, endowment, and conversion factors. It proposes an innovative approach to examine and enhance the quality of life and wellbeing of individuals. This reflective paper provides an overview of the capability approach, critically analyses population health from the theoretical lens of the capability approach and highlights the relevance of this approach to achieving the Sustainable Developmental Goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".