Human Becoming and 80/20: An Innovative Professional Development Model for Nurses
Bibliographic record
Abstract
The authors describe a study that evaluated implementation of a professional development model in which nurses spend 80% of their salaried time in direct patient care and 20% of their salaried time on professional development. The professional development time includes focused learning about patient-centered practice guided by the human becoming nursing theory. A qualitative descriptive preproject-process-postproject method and a longitudinal, repeated measures, descriptive-comparative method were used to answer the research questions. Participants were 33 nurses, 11 other nurse leaders and health professionals, and 55 patients and family members. The findings show that on the study unit overtime hours decreased significantly, the education hours were sustained throughout the study period, workload hours per patient day increased significantly, sick time stayed low, patient satisfaction scores increased, staff satisfaction scores were significantly higher than for comparator groups, and turnover was non-existent among study participants in year 2. Average variable direct labor cost increased over time, but the increase was not significantly higher than on the control units. Themes from the interviews with participants are presented. Ongoing evaluation of the model and implications for future research are discussed.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".