Predictors Of Time Spent by Nurse Practitioners In Primary Care, Home Care And Long-Term Care On Activities In Two Canadian Provinces: Time And Motion Studies
Bibliographic record
Abstract
Aim: To identify the factors that influence the time nurse practitioners (NPs) in primary care, home care and long-term care spend on activities in two Canadian provinces. Background: Little is known about how patient, provider, and organizational characteristics influence the time NPs spend when caring for patients in primary care. Methods: Time and motion studies (n=30 NPs, 829 hr, 14 practices). Data were collected in Québec from May 2017–May 2018 and in Ontario from March–November 2015. Descriptive statistics and linear regression analysis were completed. Findings: NPs spent 66% (Ontario) to 68% (Québec) of work time on clinical activities. Mean time spent by NPs in patient encounters in Québec varied by setting (long-term care: 13 min 21 s; primary care: 22 min 10 s; and home care: 26 min 25 s). In Ontario, time spent by NPs in patient encounters averaged 25 min 48 s in primary care. In Québec, significant associations were found between number of clinical and non-clinical activities, health maintenance/wellness visit, chronic illness follow-up, patient gender (woman), urban location, and multiple informants in the exam room and NP time spent on activities. In Ontario, associations were identified between number of clinical and non-clinical activities, chronic illness follow-up, NP gender (woman), and acute/minor injury and NP time spent on activities. Conclusion: Time NPs spend on activities differed depending on patient, provider, organizational and health system characteristics. NP activities cut across all role dimensions. The practice setting, number of clinical and non-clinical activities, and chronic illness follow-up were significant predictors of time spent on activities. Our study provides a comprehensive overview of NP activities and the factors that influence time spent on these activities while working in health systems with more and less restrictive scope of practice regulations, and with a wide range of patient populations in primary care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".