Patient Complexity in Nurse Practitioner-Led Clinics in Ontario
Bibliographic record
Abstract
Aim: To assess the level of complexity of patients in Nurse Practitioner-Led Clinics (NPLCs). Background: Complexity has emerged as a key issue in primary health care. There is no easily accessible dataset to evaluate the level of complexity and needs of their patients in this clinic model. Methods: NPs at four NPLCs assessed patients during the study period with the PCAM, which is a reliable and valid tool that is used to evaluate physical and biopsychosocial elements contributing to complexity. A total of 677 PCAM evaluations were completed which were analyzed to determine the level of complexity of patients in NPLCs. Findings: The results showed that patients with the highest complexity are those with high social/economic needs: low education; low income; low levels of employment. Conclusions: These results demonstrate the potential positive impact of an interdisciplinary team and may inform changes to the allocation of resources in the clinic settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".