Exploring sources of knowledge utilized in practice among Jordanian registered nurses
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Understanding sources of knowledge used in everyday practice is very helpful in improving the quality of health care services. There is a consensus in the literature that nurses mostly relied in their practice on experiential knowledge gained through their interactions with other members of health care professionals and patients. The general aim of this study is to explore the sources of knowledge Jordanian registered nurses use during their practice. METHOD: A descriptive correlational design was used to collect data from 539 Jordanian registered nurses from 10 hospitals using a self-administered questionnaire. RESULTS: The mean year of experience of the sample was 7.08 years. Of the 615 questionnaires distributed, 555 were returned. This yields a response rate of 87.6%. Results revealed that the top five ranked sources used by Jordanian registered nurses include: the information that nurses learned during nursing education, personal experience in nursing over time, what was learned through providing care to patients, information gained through discussion between physicians and nurses about patients, and information from policy and procedure manuals. CONCLUSION: Jordanian registered nurses recognize the value of research and that research utilization (RU) is an important issue and must not be ignored. The study has many implications for practice, education and research. Health care managers and decision makers need to play a more visible and instrumental role in encouraging RU to improve patients' quality of life.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".