Effects of a care workshop on caring behavior and job involvement of nurses
Bibliographic record
Abstract
Objective : The aim of the study was to determine the effectiveness of a care workshop in improving caring behaviors and job involvement among nurses. Methods : A quasi-experimental designed study was used in which 338 nurses, on a hospital in Taiwan were surveyed before and after a care workshop. The intervention consisted: (1) formal educational sessions twice a week for six weeks, (2) a loving care mentorship activity, and (3) posts of exemplary caring behavior and stories. The socio-demographics, the Modified Caring Assessment Report Evaluation Q-sort, and the Modified Job Involvement Instrument were used. Descriptive statistics were analyzed to evaluate participant demographic characteristics. Paired t tests were used to determine the effects of a care workshop on caring behaviors and job involvement of nurses. Results : The participants’ ages ranged from 20 to 45 years, with a mean of 30.67 years (SD = 5.86). Nurse caring behavior and job involvement were negatively correlated on the pretest ( p < .01) and positively correlated on 6-week posttest ( p < .01). Nurses exhibited more caring behaviors after the intervention than did nurses before the intervention ( p < .001). Increased job involvement scores were observed after the intervention compared with the scores before the intervention ( p < .001). Conclusions : The findings of this study, suggest that a care workshop intervention focused at nurses can be effective in improving nurses caring knowledge and attitudes regarding patient-center care and in increasing job involvement among nurses. Further research is required to explore the long-term efficacy of the intervention in the organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".