Inclusion of vulnerable groups in health policies: Regional policies on health priorities in Africa
Bibliographic record
Abstract
BACKGROUND: If access to equitable health care is to be achieved for all, policy documents must mention and address in some detail different needs of groups vulnerable to not accessing such health care. If these needs are not addressed in the policy documents, there is little chance that they will be addressed at the stage of implementation. OBJECTIVES: This paper reports on an analysis of 11 African Union (AU) policy documents to ascertain the frequency and the extent of mention of 13 core concepts in relation to 12 vulnerable groups, with a specific focus on people with disabilities. METHOD: The paper applied the EquiFrame analytical framework to the 11 AU policy documents. The 11 documents were analysed in terms of how many times a core concept was mentioned and the extent of information on how the core concept should be addressed at the implementation level. Each core concept mention was further analysed in terms of the vulnerable group in referred to. RESULTS: The analysis of regional AU policies highlighted the broad nature of the reference made to vulnerable groups, with a lack of detailed specifications of different needs of different groups. This is confirmed in the highest vulnerable group mention being for 'universal'. The reading of the documents suggests that vulnerable groups are homogeneous in their needs, which is not the case. There is a lack of recognition of different needs of different vulnerable groups in accessing health care. CONCLUSION: The need for more information and knowledge on the needs of all vulnerable groups is evident. The current lack of mention and of any detail on how to address needs of vulnerable groups will significantly impair the access to equitable health care for all.
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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 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".