Perceived representativeness, usefulness and impacts of using the nursing activities score as part of a workload readjustment initiative: A mixed methods study
Bibliographic record
Abstract
Objective: Nursing workload has been shown to have negative impacts on job satisfaction, retention and turnover. The NAS is one of existing tools targeting nursing workload quantification. Although tested in multiple settings, few studies explored nurses’ perception of its representativeness of workload, and its impacts when used to readjust nurse/patient ratios. This study was conducted to validate nurses’ perception of representativeness and potential usefulness of the Nursing Activities Score (NAS) in regard to nurses’ perceived workload and explore its impacts as part of a workload readjustment initiative.Methods: A mixed method design was selected, combining semi-structured interviews (n = 13), and secondary analysis of project monitoring data for an entire intensive care unit (n = 139). Statistical analysis was performed using SPSS v.22 software for quantitative data, and qualitative data was analyzed through NVivo 12 software using a thematic deductive-mixed content analysis, aiming for the emergence of recurring themes. Results: When taken as a whole, the NAS is perceived to be representative of nursing workload. However, validity concerns were identified at the tool completion level, notably in regard to improper documenting of events and fundamental understanding of the tool by nurses. Numerous impacts related to the use of the NAS were also identified.Conclusions: Although the NAS appears to adequately represent nursing workload, its pertinent utility remains debatable due to high subjectivity described in this study. The sole use of the tool for patient assignment is therefore questionable.
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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 | 0.069 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 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".