Does contracting of health care in Afghanistan work? Public and service-users' perceptions and experience
Bibliographic record
Abstract
BACKGROUND: In rebuilding devastated health services, the government of Afghanistan has provided access to basic services mainly by contracting with non-government organisations (NGOs), and more recently the Strengthening Mechanism (SM) of contracting with Provincial Health Offices. Community-based information about the public's views and experience of health services is scarce. METHODS: Field teams visited households in a stratified random sample of 30 communities in two districts in Kabul province, with health services mainly provided either by an NGO or through the SM and administered a questionnaire about household views, use, and experience of health services, including payments for services and corruption. They later discussed the findings with separate community focus groups of men and women. We calculated weighted frequencies of views and experience of services and multivariate analysis examined the related factors. RESULTS: The survey covered 3283 households including 2845 recent health service users. Some 42% of households in the SM district and 57% in the NGO district rated available health services as good. Some 63% of households in the SM district (adjacent to Kabul) and 93% in the NGO district ordinarily used government health facilities. Service users rated private facilities more positively than government facilities. Government service users were more satisfied in urban facilities, if the household head was not educated, if they had enough food in the last week, and if they waited less than 30 minutes. Many households were unwilling to comment on corruption in health services; 15% in the SM district and 26% in the NGO district reported having been asked for an unofficial payment. Despite a policy of free services, one in seven users paid for treatment in government facilities, and three in four paid for medicine outside the facilities. Focus groups confirmed people knew payments were unofficial; they were afraid to talk about corruption. CONCLUSIONS: Households used government health services but preferred private services. The experience of service users was similar in the SM and NGO districts. People made unofficial payments in government facilities, whether SM or NGO run. Tackling corruption in health services is an important part of anti-corruption measures in Afghanistan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".