Determining resource intensity weights in ambulatory chemotherapy related to nursing workload
Bibliographic record
Abstract
Ontario cancer programs aim to deliver high-quality nursing care and treatment that is safe for patients and staff. The reality of health care is that financial constraints, inherent in the delivery of care, require that funding mechanisms count not only the cost of drugs, but factors such as pharmacy and nursing human resource costs. While some organizations have developed patient classification systems to measure nursing intensity and workload, these systems apply primarily to inpatient populations, and are fraught with numerous challenges, such as the need for nurses to document to justify the workload required for care. The purpose of this paper is to outline the methodology and engagement of nurses to develop regimen-based resource intensity weights that can be applied to ambulatory chemotherapy suites. The methodology included determination of workload related to nursing time to prepare, teach, counsel and assess patients, as well as time to gather supplies, access lines, monitor, manage adverse reactions, manage symptoms and document care. Resource intensity weights provide better measures of the complexity of care required by cancer patients in ambulatory settings.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".