Factors Influencing Panel Size of Primary Care Nurse Practitioners
Bibliographic record
Abstract
The aim of this study was to investigate organizational and practitioner-related factors influencing the panel size of primary care nurse practitioners (PC-NPs) in Quebec (Canada). Cross-sectional study. This secondary analysis was based on a cross-sectional study that used a self-administered online questionnaire available from March to April 2022 to assess the work conditions of NPs in Quebec. A multiple regression analysis was conducted on a subset of 321 PC-NPs to predict panel size and associated factors. Among 321 PC-NPs, with a mean of 4.6 years of experience as NP, the average panel size was 344 patients. Factors significantly associated with a greater panel size were a higher number of years of experience as a NP (p < 0.001), a higher number of years spent in the current organization (p < 0.001) and a higher number of patients seen in an average day (p < 0.001). Our study provided a measure of the PC-NP panel size in the province of Québec highlighting their essential role in primary health care. The results suggest that policymakers and administrators should focus on enhancing the experience of their PC team, ensuring employment stability and providing adequate time for patient appointments to optimize PC-NP panel size and enhance service capacity to increase access to primary health care.
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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".