Challenges and opportunities in addressing social determinants of child health in Cambodia: perspectives and experience of frontline providers in two health districts
Bibliographic record
Abstract
OBJECTIVES: Other forces related to socio-economic and cultural factors, besides biomedical and behavioural fields, also influence health but receive little attention in health research. This study aims to illuminate social determinants of health and to identify challenges and opportunities in addressing social determinants of child health (SDCH) in rural Cambodia. METHODS: This is a qualitative study based on interviews of frontline primary health care providers, health officials, local authorities and community volunteers in two health districts in Cambodia. The data were supplemented by secondary data on different aspects of the districts and Cambodia. RESULTS: Poverty, lack of basic commodities and adverse social conditions remained problems for population health. While access to health services was considered adequate, households and communities had several major risk exposures. Challenges in addressing SDCH were the high prevalence of social and household adverse conditions, and the lack of training of providers, of information about social services, of effective coordination and of trust in public services. Opportunities were present, including social services being existent albeit poor functioning, the traditional practice of social inquiry, existing frontline providers being open to further information and training, existing subnational coordination bodies at district and provincial levels, and use of evidence in planning and resource allocation. CONCLUSION: Addressing SDCH requires broad and coordinated efforts of stakeholders from multiple sectors. Among the prerequisites are to leverage the existing structures and mechanisms, training primary health care providers and providing them with adequate information about local resources and available supports. Improving social care services and infrastructures requires strong coordination, planning and adequate resource allocation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".