Exploring Factors that May Influence Ontario Nurse Practitioners’ Patient Panel Size in Primary Healthcare Settings
Bibliographic record
Abstract
Limited knowledge exists about the factors that may influence nurse practitioner (NP) patient panel size. Patient panel size refers to the number of patients for whom a NP is their usual care provider. Increased knowledge of these factors may improve patient care, NP practice, and primary health care (PHC) workforce planning. Two hundred and eighty-three NPs working in Ontario PHC were surveyed to explore patient, NP, and organizational factors that may influence NP patient panel size. Three factors were associated with NP panel size. Higher percentages of certain health conditions and/or longer appointment time for multi-morbid and palliative care were associated with smaller NP patient panel size. NPs who worked more hours per week had larger patient panels. Also, the PHC practice model was related to NP patient panel size, which was largest in NP-led clinics. Decision makers can use these findings to support optimization of NP patient panel size.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".