Essential Professional Nursing Practices in mental health: A cross‐sectional study of hospital inpatient care
Bibliographic record
Abstract
Quality organizational structures and nursing practices are key to positive patient outcomes. Whereas structures have been largely studied over the past few decades, less is known of the nursing practices that account for patient outcomes, such as patient satisfaction. This is especially true in psychiatric, mental health care settings. The aim of the present study is to determine the relative importance of eight Essential Professional Nursing Practices (EPNPs) on the satisfaction of hospitalized patients on mental health care units. A cross-sectional design was selected; 226 point-of-care mental health nurses completed the online EPNP questionnaire in Spring 2015. Statistical analyses included MANOVAs and a 2-step linear regression. A significant relationship was found between university preparation and scores on two EPNP subscales: autonomous decision-making and practicing with competent nurses. Scores on patient advocacy and control over practice subscales were significantly related to nurse-rated patient satisfaction. The findings reinforce the positive link between university education and the work of nurses and highlight the power dynamics that are salient in mental health care. The pertinence of EPNPs in psychiatric settings is brought to the fore, with practices of patient advocacy and nurse control over care examined in relation to empowerment. Implications for clinical and administrative leaders are addressed, with a focus on strategies for empowering patients and nurses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".