Uptake of patient enrolment in primary care and associated factors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patient enrolment in primary care refers to the formal process of registering patients with a specific primary care provider, team, or practice. This approach is often expected to enhance continuity and coordination of care. However, limited information exists on the uptake of patient enrolment and its associated characteristics. This review aimed to estimate the uptake of patient enrolment in primary care and examine factors associated with decisions around enrolment. METHODS: Eight electronic databases (PubMed, Cochrane Register of Systematic Reviews, Embase, CINAHL, PsycINFO, PAIS, Web of Science, and Scopus) were searched for peer-reviewed articles published from January 2014 to July 2024. Findings from included studies were extracted and synthesised, with uptake estimated through meta-analysis and factors associated with enrolment summarised narratively. REVIEW REGISTRATION: PROSPERO CRD42024597078. RESULTS: Ten studies across nine publications were included. Of these, eight studies with 27,919,216 participants were included in the meta-analysis. The results showed a pooled patient enrolment uptake rate of 71.4% (95% Confidence Interval [CI]: 13.6-97.5%). There was no significant difference in enrolment rates between population-wide and program-based enrolment (72.4% vs. 73.5%; p = 0.980). Several associated factors were identified in three publications. Women showed higher enrolment rates than men (adjusted odds ratio [aOR] = 1.07, 95% CI: 1.07-1.08), while recently arrived immigrants in a country had lower enrolment rates than the established population (aOR = 0.40, 95% CI: 0.40-0.41). Patients living in small urban/suburban/rural areas had higher enrolment rates than those in large urban/metropolitan regions (aORs: 1.17-2.18). Higher socioeconomic level was associated with increased rates of enrolment. Patients with some specific chronic health conditions, such as those with diagnosed mental illness or substance use disorders, had lower enrolment rates. CONCLUSIONS: The findings reveal that more than two-thirds of patients were enrolled with a primary care provider or practice; enrolment was influenced by demographic, geographic, socioeconomic, and clinical factors. Lower enrolment among men, recent immigrants, individuals living in large urban/metropolitan areas, lower socioeconomic groups, and those with certain health conditions may indicate potential barriers to health service access and opportunities for enrolment. Addressing these disparities is essential to promote equitable access and enhance opportunities for continuity and coordination of primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".