Factors influencing nurse practitioner panel size in team-based primary care: a qualitative case study
Bibliographic record
Abstract
BACKGROUND: Lack of access to health care is a worldwide public health crisis. In primary care it has led to increases in the implementation of nurse practitioners and heightened interest in their patient panel capacity. The aim of this study was to examine factors influencing nurse practitioner patient panel size in team-based primary care in Ontario, Canada. METHODS: We used a multiple case study design. Eight team-based primary care practices including rural and urban settings were purposively selected as cases. Each case had two or more nurse practitioners with a minimum of two years experience in the primary care setting. Interviews were conducted in-person, audio recorded, transcribed and analysed using content analysis. RESULTS: Forty participants, including 19 nurse practitioners, 16 administrators (inclusive of executives, managers, and receptionists), and 5 physicians were interviewed. Patient, provider, organizational, and system factors influenced nurse practitioner patient panel size. There were eight sub-factors: complexity of patients' health and social needs; holistic nursing model of care; nurse practitioner experience and confidence; composition and functioning of the multidisciplinary team; clerical and administrative supports, and nurse practitioner activities and expectations. All participants found it difficult to identify the panel size of nurse practitioners, calling it- "a grey area." Establishing and maintaining a longitudinal relationship that responded holistically to patients' needs was fundamental to how nurse practitioners provided care. Social factors such as gender, poverty, mental health concerns, historical trauma, marginalisation and literacy contributed to the complexity of patients' needs. Participants indicated NPs tried to address all of a patient's concerns at each visit. CONCLUSIONS: Nurse practitioners have a holistic approach that incorporates attention to the social determinants of health as well as acute and chronic comorbidities. This approach compels them to try to address all of the needs a patient is experiencing at each visit and reduces their panel size. Multidisciplinary teams have an opportunity to be deliberate when structuring their services across providers to meet more of the health and social needs of empanelled patients. This could enable increases in nurse practitioner 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".