Differences in Preventive Care Uptake in Attached and Unattached Rural-Living Residents
Bibliographic record
Abstract
Preventive care services are crucial for overall health, yet, rural communities experience low rates of preventive service use. Primary care providers are pivotal in facilitating preventive service uptake (e.g., vaccinations, screenings) but shortages have left 1 in 5 Canadians without a primary care provider. The aim of this study was to compare preventive care uptake between BC rural residents attached and unattached to a primary care clinician. A quantitative cross-sectional survey of rural patients, both with (attached) and without (unattached) a primary care provider, was conducted from July to Sept 2022. Participants completed measures assessing prevention activity completion, priorities, and prevention activity self-efficacy. Descriptive statistics were used to compare preventive care completion and attachment status. A total of 516 rural residents (301 attached; 215 unattached) completed the survey (M age = 50.63 years; 74.4% female). Unattached patients reported lower prevention service completion rates (M = 51%) compared with attached patients (M = 63%; p < .001), although there was no significant difference in the number of prevention priorities. Self-efficacy for provider communication (p < .001), managing chronic illness (p = .002), getting vaccines (p < .001), and completing preventive screening (p < .001) was lower among unattached compared with attached participants. The results indicate a suboptimal uptake of preventive care in rural communities. Furthermore, they highlight a concerning gap in uptake between attached and unattached patients and provide strategic information for developing and implementing preventive care policy and programs, a pressing need given the persistent provider shortage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".