Concept Analysis and Proposed Definition of <i>Community Health Center</i>
Bibliographic record
Abstract
Background: Community health centers (CHCs) have been used for delivery of health services since the 1920s and originally were intended to provide care to underserved populations. CHCs have become an integral part of healthcare systems in many countries; however, the term CHC is used synonymously with other concepts and there is no clear definition for CHC. The purpose of our concept analysis was to determine how CHCs are described in the literature and to develop a concept definition for CHC. Methods: Informed by the 8-step process described by Walker and Avant, we searched for literature spanning disciplines within health, business, and policy. We used a systematic review process to identify a range of peer-reviewed articles that help illustrate the attributes, antecedents, and consequences of CHCs. A total of 102 articles from 7 databases were included in our concept analysis. Results: We distinguished 6 attributes of a CHC: primary care; accessibility; preventative care; defined population; health promotion; and comprehensive and integrated care. About 4 antecedents fundamental to a CHC included: secure funding; vision and support; adequate human resources; and governance structure. Consequences of CHCs are improved health outcomes, efficiency, and cost-effective provision of healthcare services. Conclusions: Our concept analysis revealed core characteristics of CHCs that assisted us in synthesizing a concept definition for CHC. These characteristics and our proposed definition will help provide clarity on the concept of CHC to benefit evaluation, research, and policy development of CHCs.
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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 | 0.060 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".