Online Workload Measurement Index for NPs: Study of Acceptability
Bibliographic record
Abstract
Aim: To examine the acceptability of using a workload measurement tool for nurse practitioners (NPs). Background: There are important pressures in healthcare systems in Canada and internationally to increase the number of patients seen by healthcare providers in primary care, including NPs, as a strategy to increase care access. NPs work in various primary care settings, and with diverse patient populations. Previous research has found that patient, NP and organizational factors influence NP workload, with NPs utilizing both clinical and non-clinical activities to address patient care needs. However, we have a limited understanding of the factors that influence the acceptability and adequacy of measuring NP workload. Implementing an online NP workload measure in community-based primary care is complex. Methods: Qualitative descriptive approach with individual semi-structured interviews (n = 13) with NPs and decision-makers. Data were collected from May–July 2024 in Québec, Canada. After a deductive coding strategy based on the Theoretical Framework of Acceptability, an inductive approach was used, to enable themes to emerge from the data. Interviews were coded individually by two researchers, and a third researcher reviewed the coding for consistency. Findings: NPs and decision-makers (n = 13) reported that the workload measure was easy to use and comprehend. Participants believed the measure represented NPs’ work, emphasizing that the recorded data enabled them to observe how their workload was spread among various activities. The tool's content and the brief data input time (five minutes per day) were perceived as facilitators of acceptability. A few minor obstacles were identified, including software issues, and recommendations were made for enhancements. Conclusion: The study provides an in-depth understanding of the acceptability of using a workload measurement tool for NPs. Additional research is needed to explore the acceptability of implementing the tool of NP workload in other clinical areas, including mental health.
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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 | 0.040 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".