Social Determinants of Health among Health Workers in a Tertiary Health Institution in Anambra State, Nigeria: A Pilot Study
Bibliographic record
Abstract
Background Social determinants of health (SDH) are a critical theme for health equality for Nigeria. Nigeria is one of the countries in the world which is far from achieving targets of 2030 SDG 3 due to this inequity in health. Methods This is a cross-sectional pilot survey designed to collect information about the SDH, strengths, challenges and perceived areas to be addressed among different cadres of healthcare workers at the Chukwuemeka Odumegwu Ojukwu University Teaching Hospital (COOUTH) Awka in the South eastern part of Nigeria. This is a cross-sectional pilot survey among the different cadres of healthcare workers. Results The elements of SDH are vital for the continuing well-being of health workers because of their role in attending to the totality of their community. From our study, two elements of SDH (family and physical activity) had the greatest level of confidence (44% and 26% respectively), while the justice system (police and the legal issues) had the lowest areas of confidence (2% each), but the greatest strength of the community were found in education (70%) and family (58%). Conclusions Policies in general need to be implemented to address the economic instability in order to yield positive outcomes towards education, security of lives and property, food security and affordable healthcare and transportation. Reducing health inequities in Nigeria will depend on a focus addressing the social circumstances of individuals, families and communities using equity-based approaches on the broader structural environment. The role of formal and informal educational strategies will be beneficial in the highlighted social, economic and political factors from this study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".