Quality of Care in Modern Contraceptive Service Delivery in the Public and Private Sector: A Cross Sectional Study in Indonesia
Bibliographic record
Abstract
The Government of Indonesia has established a set of program interventions to enhance the quality of family planning services. The program gives preferences to the acceptance of family planning services and the readiness of the supply side. This study is intended to better understand the extent to which the public and private sectors deliver quality family planning services in 4 selected provinces within Indonesia. The six elements of quality of care (Bruce, 1990) were utilized as the study framework. The study confirmed that the mean of all six elements of quality of care are significant (alpha =0,05) in two out of the four study sites. From the clients’ point of view, information on contraceptive choices was the most neglected aspect in the public health facilities, while ‘follow up and a continuity mechanism’ was most neglected in the private health facilities. The equity index showed a substantial difference in the overall quality of care between the two types of health facilities (public= 4.53 versus private= 5.34). As far as health providers are concerned, quality of care is still below the optimum standard. Emphasis should be given to formally shape the desired health provider behavior and find a way to create an ‘after-sales-service’ scheme. The concept of quality goals need to be mindful of program maturity across regions. Periodic monitoring and evaluation is required to ensure more client satisfaction which leading to more sustained use of modern contraceptives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".