Nurse practitioners in Ontario primary healthcare: Referral patterns and collaboration with other healthcare professionals
Bibliographic record
Abstract
The purpose of this study is to examine referrals of nurse practitioners providing primary healthcare (PHC NPs) to better understand how PHC NPs collaborate with other healthcare professionals and contribute to interprofessional care. The analysis is based on the data from a survey of 378 PHC NPs registered in Ontario, Canada in 2008. Overall, 69% of PHC NPs made referrals to family physicians (FPs) and 67% of PHC NPs received referrals from FPs. Almost 50% of PHC NPs had bidirectional referrals between them and FPs. Eighty-nine percent of PHC NPs made referrals to specialist physicians. Bidirectional referrals between PHC NPs and social workers and mental health workers were common in family health teams and community health centers. Patterns of referrals (bidirectional, unidirectional and no referrals) between PHC NPs and FPs, social workers, mental and allied health workers in various practice settings indicate development of collaborative relationships between PHC NPs and other healthcare professionals and reflect the influence of practice models on delivery of interprofessional care. These findings are discussed in light of the development of NPs' role and integration of PHC NPs in the Ontario healthcare system. Implications for policy changes and future research are also suggested.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".