Nurses’ professional values on patient care provisions and decisions
Bibliographic record
Abstract
Objective: To determine the relationship of professional value system of nurses to their duties and functions specifically on patient care provisions and decisions among selected government hospitals in Hail city, Kingdom of Saudi Arabia.Methods: The study utilized a Descriptive Correlational method of research inquiry where a 26-item questionnaire on nurse professional value system (NPVSR) were distributed to 150 staff nurses employed in 3 government hospitals in the City of Hail after which, their responses were correlated using Pearson r against their demographic profiles such as gender, age, religion, ethnicity, educational attainment, years of practice, current unit or ward assignment and current rank or position.Results: Based on the responses of the 150 participants, the results showed that there was no significant relationship that exists between their demographic profiles to their value systems on different patient care provisions and decisions during their clinical duties and employment as staff nurses.Conclusions: The study implicated that the value systems of the participants are not dependent or influenced largely or directly by their gender, age, religion, ethnicity, years of practice, educational attainment, current ward or unit assignment and current rank or position. The data further revealed that for this specific group of participants, their professional value system may in some other ways influenced by other factors not mentioned or included in their demographic profiles for the study.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".