Nurses' Career Commitment and Job Performance: Differences across Hospitals
Bibliographic record
Abstract
The interrelatedness of nurses' career commitment and job performance is debated. In nursing, few studies have focused on the relationship between the two concepts. A convenience sample of 640 registered nurses (RNs) from 24 hospitals was recruited. A comparative design was used to assess differences among governmental, teaching and private hospitals in regard to the concepts measured. In general, nurses were found to "agree" that they had a lifelong commitment to their careers, and that they were performing "well" their jobs in accordance with standards. Hospitals in the sample differed in most demographics except in gender, areas of work and decision-making styles. Based on the total scores of nurses' career commitment, there were no significant differences across hospitals. Based on the total scores of nurses' job performance, F-tests indicated some differences; the highest mean was at private hospitals. Using dimensional means of nurses' job performance uncovered no significant differences among hospitals. Individual items of nurses' job performance subscales differed, in some cases significantly, particularly for nurses working at private hospitals: nurses' career commitment was correlated positively and significantly with their job performance. Consistent with the current researchers' hypothesis, nurses' career commitment appears to influence job performance and is influenced by the nurses' characteristics and organizational factors in the workplace. Enhancing nurses' career commitment and their job performance should produce positive outcomes for nurses, patients and organizations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".