Roles of nurse practitioners and family physicians in community health centres.
Bibliographic record
Abstract
OBJECTIVE: To describe the models of practice used by nurse practitioners (NPs) and FPs in community health centres (CHCs), and to examine the roles of NPs and FPs in these models. DESIGN: Cross-sectional study using an organizational survey completed by managers of the CHC sites, as well as administrative data on patient sociodemographic characteristics and encounter activities. SETTING: A total of 21 CHCs (13 main sites and 8 satellite sites) operating in eastern Ontario during the period from December 1, 2006, to November 30, 2008. PARTICIPANTS: A total of 44 849 patients, 53 full-time equivalent FPs, and 41 full-time equivalent NPs. MAIN OUTCOME MEASURES: Family physicians' and NPs' models of practice, the sociodemographic characteristics and medical profiles of patients who were treated in each model of practice, and FPs' and NPs' use of time. RESULTS: Patients were attributed to 1 of 3 models of practice in CHCs based on the proportion of visits to FPs and NPs: FP care (53% of patients), NP care (29%), and shared care (18%). Patients who received care in the NP model of practice were younger and more likely to be female, be homeless, and not have postsecondary education.Patients who received care in the FP model of practice had more complex medical conditions (cardiovascular disease, mental illness, lung disease, and diabetes) and more annual visits. Patients who received care in the shared care model had intermediate profiles. Nurse practitioners performed more off-site care and walk-in visits. Family physicians and NPs spent a similar proportion of time performing various duties such as direct clinical care and administration tasks. CONCLUSION: Although NPs mainly cared for their own patient panels (in the NP care model), they did share some patients with FPs and provide some care to patients under the FP model of practice. Patients who were cared for by FPs and NPs had quite different characteristics.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".