Comprehensiveness in Primary Care: A Scoping Review
Bibliographic record
Abstract
Policy Points Efforts to address a perceived decline of comprehensiveness in primary care are hampered by the absence of a clear and common understanding of what comprehensiveness means. This scoping review mapped two domains of comprehensiveness (breadth of care and approach to care) as well as a set of factors that enable comprehensive practice. The resulting conceptual map supports greater clarity for future use of the term comprehensiveness, facilitating more precisely targeted research, practice, and policy efforts to improve primary care systems. CONTEXT: Associated with system efficiency and patient-perceived quality, comprehensiveness is widely recognized as foundational to high-quality primary care. However, there is concern that comprehensiveness is declining and that primary care physicians are providing a narrower range of services. Efforts to address this perceived decline are hampered by the many different and sometimes vague definitions of comprehensiveness in current use. This scoping review explored how comprehensiveness in primary care is conceptualized and defined in order to map its attributes in support of being able to more clearly and precisely define this key concept in research, practice, and policy. METHODS: We conducted a scoping review, following the methods of Arksey and O'Malley and Levac and colleagues. The search included terms for two key concepts: primary care and comprehensiveness. Developed in Ovid Medical Literature Analysis and Retrieval System Online (MEDLINE), the search was adapted for Cumulated Index in Nursing and Allied Health Literature (CINAHL) and Embase, as well as for gray literature. After a multistep review, included sources underwent detailed data extraction. FINDINGS: A total of 360 sources were extracted; 57% were empirical studies and 65% were published between 2010 and 2022. Across these sources, we identified nine attributes of comprehensiveness in primary care. We mapped these attributes into two conceptual domains: breadth of care (services, settings, health needs and conditions, patients served, and availability) and approach to care (one-stop shop, whole-person care, referrals and coordination, and longitudinal care). Additionally, we identified three enablers of comprehensiveness, namely structures and resources, teams, and competency. CONCLUSIONS: The conceptual map of comprehensiveness in primary care offers a valuable tool that supports clarity for future use of the term comprehensiveness. The domains and attributes we identified can be used to develop definitions and measures that are appropriate to research, practice, and policy contexts, enabling more precise efforts to improve primary care systems.
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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 | 0.035 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".