Career Commitment and Job Performance of Jordanian Nurses
Bibliographic record
Abstract
BACKGROUND: Career commitment and job performance are complex phenomena that have received little attention in nursing research. DESIGN AND SAMPLE: A survey was used to assess nurses' career commitment and job performance, and the relationship between the two concepts. Predictors of nurses' career commitment and job performance were also studied. A convenience sample of 640 Jordanian registered nurses was recruited from 24 teaching, governmental, and private hospitals. RESULTS: Nurses "agreed" on the majority of statements about career commitment, and they reported performing "well" their jobs. Using total scores, nurses were equal in their career commitment but they were different in their job performance; the highest mean was scored for nurses in private hospitals. Using the individual items of subscales, nurses were willing to be involved, on their own time, in projects that would benefit patient care. The correlation of the total scores of nurses' career commitment and job performance revealed the presence of a significant and positive relationship (r = .457). Nurses' job performance, gender, and marital status were the best predictors of nurses' career commitment: they explained 21.8% of variance of nurses' career commitment. Nurses' career commitment, time commitment, marital status, and years of experience in nursing were the best predictors of nurses' job performance: they explained 25.6% of variance of nurses' job performance. CONCLUSIONS: The lowest reported means of nurses' job performance require managerial interventions.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".