Implementing an Online Instrument to Measure Nurse Practitioner Workload: A Feasibility Study
Bibliographic record
Abstract
Introduction/Objectives: Nurse practitioners (NPs) improve access to care in community-based primary care. Determining an appropriate workload for NPs is complex. The number of patients seen by NPs represents an important consideration. We sought to determine the feasibility, acceptability and appropriateness of implementing the online NP workload measurement index (NP-WI). Methods: Feasibility study supported by the Theoretical Framework of Acceptability, conducted across 3 health regions in Québec, Canada. Data were collected from January to July 2024 using the online NP-WI ( n = 66), 8-item acceptability questionnaire ( n = 47), weekly implementation team meetings with NPs and decision-makers ( n = 11), field notes and interviews ( n = 13). Data analysis completed using descriptive statistics and content analysis, with data integration using joint displays. Results: NPs indicated that the NP-WI was easy to use. Acceptability scores were positively rated. Daily data entry took 5 to 7 min to complete. NPs deemed a 4-week collection period sufficient to capture a representative workload sample. The NP-WI captured patient, provider and organizational characteristics and the number of patients seen by NPs. Conclusions: NP-WI implementation was feasible. The instrument can support healthcare workforce planning with more adequate estimations of NP workload in community-based primary care, and provide greater equity in resource allocation and distribution of NP workload.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".