High performing primary health care organizations from patient perspective: a qualitative study in China
Bibliographic record
Abstract
BACKGROUND: There is a global call to build people-centred primary health care (PHC) systems. Previous evidence suggests that without organization-level reform efforts, the full potential of policy reforms may be limited. This study aimed to generate a profile of high performing PHC organizations from the perspective of patients. METHODS: We conducted semi-structured interviews with 58 PHC users from six provinces (Shandong, Zhejiang, Shaanxi, Henan, Shanxi, Heilongjiang) in China using purposive and snowball sampling techniques. Transcription was completed by trained research assistants through listening to the recordings of the interviews and summarizing them in English by 30-s segments to generate the narrative summary. Informed by the Classification System of PHC Organizational Attributes, thematic analysis aimed to identify domains and attributes of high performing PHC organizations. RESULTS: A profile of a high performing PHC organization with five domains and 14 attributes was generated. The five domains included: (1) organizational resources including medical equipment, human and information resource; (2) service provision and clinical practice including practice scope, internal integration and external integration; (3) general features including location, environment and ownership; (4) quality and cost; and (5) organizational structure including continuous learning mechanism, administrative structure and governance. CONCLUSIONS: A five-domain profile of high performing PHC organizations from the perspective of Chinese PHC users was generated. Organizational resources, service delivery and clinical practices were most valued by the participants. Meanwhile, the participants also had strong expectation of geographical accessibility, high quality of care as well as efficient organizational structure. These organizational elements should be reflected in further reform efforts in order to build high performing PHC organizations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".