Importance of Client Orientation Domains in Non-Clinical Quality of Care: A Household Survey in High and Low Income Districts of Mashhad
Bibliographic record
Abstract
Responsiveness introduced by WHO as a key indicator to assess the performance of health systems and measures by common set of domains that are categorized in to two main categories "Respect for persons" and "client orientation". This study measured importance of client orientation domains in high and low income districts of Mashhad. In this cross-sectional and explanatory study, Sample of 923 households were selected randomly from two high and low income districts of Mashhad. World Health Organization (WHO) questionnaire was used for data collection. Standard frequency analyses and Ordinal logistic regression (OLR) was employed for data analysis. In general, respondents selected quality of basic amenities as the most important domain and access to social support networks was identified as the least important domain. Households in high income area scored higher domains of prompt attentions and choice Compared to low income. There was a significant relationship between variables of ages, having member that need to care and self-assessed health with the ranking of client orientation domains.Study of households' view on ranking of non-clinical aspects of quality of care, especially when faced with limited resources, can help to conduct efforts towards subjects that are more important, and lead to improve the health system performance and productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".