Impact of the COVID-19 pandemic on physical activity among older adults with cancer in a central Canadian province: Results from a survey study
Bibliographic record
Abstract
Hospital administrators have a great interest in obtaining a valid and reliable nursing workload measurement to help determine the hours of care needed per patient; one such method available is the GRASP Workload Measurement System. At Sunnybrook Health Sciences Centre, nurses' GRASP compliance and accuracy varies and often does not meet the target of 90%. The target assists the organization in estimating ongoing nursing workload and patients' care needs, while ensuring the provision of safe and appropriate care that is fiscally responsible. The objective of the quality initiative reported in this paper was to identify the facilitators and barriers that influence nurses' completion of GRASP. The quality improvement project was conducted using a mixed-method design with a sample of 28 nurses working in oncology acute care and palliative care inpatient units. The Theoretical Domain Framework (TDF), often used in behaviour change studies, was used in designing the questionnaire survey and interview questions that listed pertinent and measurable factors that may influence nurses' GRASP completion. Facilitators included: nurses' knowledge about the role GRASP has in funding and staffing levels, job responsibility, and perception of GRASP as a potential tool to organize work. Barriers identified by nurses included insufficient GRASP knowledge, limited access to workstations and computers, GRASP tool elements not capturing the complexity of the nursing work, time constraints, increased patients' acuity, and care demands. In addition to the Theoretical Domain Framework, the Normalization Process Theory was used to guide the implementation and evaluation of the recommendations to enhance nurses' GRASP compliance and adherence practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".