Predictors of Professional Nursing Practice Behaviors in Hospital Settings
Bibliographic record
Abstract
BACKGROUND: Many hospital nurses perform isolated, routine tasks, rather than use their professional training, because they are subject to control by organizational and medical divisions of labor. The environment may interfere with a nurse's ability to practice autonomously and according to professional standards. OBJECTIVES: The purpose of the study was to explore how certain factors in the environment and personal characteristics interact to affect hospital nursing practice behaviors. METHODS: The study used a nonexperimental, comparative design. Surveys were sent to a random sample of 500 nurses throughout the state of Michigan. Three instruments, measuring structural empowerment, self-efficacy for nursing practice, and professional practice behaviors, were included. Path analysis was used for statistical analysis. RESULTS: Three hundred sixty-four nurses responded (73%), of whom 251 provided usable protocols for the final analysis. Environmental factors (structural empowerment) contributed both directly to professional practice behaviors as well as indirectly through self-efficacy. Self-efficacy mainly exerted its effect as a mediator in the relationship between environmental factors and practice behaviors. Support for the proposed theoretical model was mixed, although the proposed model fit the data well (chi = 11.02 [(5, N = 251), p < .05, CFI = .999, NNFI = .991, RMSEA = .069]). An alternative model emerged from the data analysis. DISCUSSION: Nurses may practice more professionally when the environment provides opportunities and power through resources, support, and information. Self-efficacy may contribute to professional practice behaviors, especially in an environment that has the requisite factors that provide empowerment.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".