The development of a workload measurement resource for community health nurses within the Central Regional Integrated Health Authority of Newfoundland and Labrador
Bibliographic record
Abstract
Background: Over the past decade, the roles of Community Health Nurses (CHNs) have changed significantly. Patients who are discharged from hospitals and referred to CHNs have more complications and it is important to determine ways to examine the means by which the CHN can meet the needs of these clients. CHNs have requested a review to determine if staffing levels reflect the quantity and complexity of the client’s they are responsible for. Currently a workload measurement tool does not exist in the Central Regional Integrated Health Authority (CRIHA) of Newfoundland and Labrador (NL). Therefore, it was determined that a need for the development a workload measurement tool to use in community health in the CRIHA of NL was necessary. Purpose: The purpose of this practicum project was to develop a workload measurement tool to help better understand the caseloads of CHNs within the CRIHA of NL. Methods: Two methods were used; a literature review and consultations with key stakeholders. The framework used to guide this practicum project was the Community Health Nurses of Canada (CHNC) Professional Practice Model and Standards of Practice. In addition, a description of how the advanced practice nursing (APN) competencies were demonstrated by the practicum student is provided. Results: Several workload measurement tools were identified in the literature. The Client Audit Community Care Workload Measurement Tool (CACCWM) previously developed by Cawthorne and Rybak (2008) in Stoney Plains Alberta was adapted to demonstrate the workload of CHNs in CRIHA of NL. Additionally, based on consultations with CHNs and the literature review an extra duties sheet was developed as part of the workload measurement tool. Conclusion: With the introduction and use of the workload measurement tool for CHNs in CRIHA, opportunities to improve staffing levels may be identified to reflect the quantity and complexity of clients on the CHNs caseload. The four APN competencies were demonstrated throughout this practicum project.
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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 | 0.042 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".