The Psychometric Properties and the Development of the Indicators of Quality Nursing Work Environments in Taiwan
Bibliographic record
Abstract
BACKGROUND: The nursing shortage in medical institutions in Taiwan averaged 9% in 2012, considerably higher than the 5% indicated in the literature. As a result, many hospitals have been forced to close wards or reduce beds. Despite the acute need, the percentage of registered nurses who are employed as nurses in Taiwan (60.4%) is considerably lower than those in Canada or the United States. This low rate may be because of the poor working environment for nurses in Taiwan. PURPOSE: This study aimed to develop a set of nursing work environment quality indicators for Taiwan and to test the reliability and validity of the resulting survey tool. METHODS: Multiple methods were used in this study. In Phase 1, we organized an expert panel, reviewed the literature, and conducted seven rounds of expert panel discussion and six focus group discussions with nursing directors. The goal was to draft indicators representing a quality nursing work environment to fit current conditions in Taiwan. In Phase 2, we conducted an expert review for content validity, held three public hearings, and conducted a survey. Four hundred twenty-seven questionnaires were sent out, with 381 returned. The goal was to test the content validity, construct validity, and internal consistency reliability. RESULTS: The study produced a set of indicators of a quality nursing work environment with eight dimensions and 65 items. The content validity index for importance and suitability dimensions were 1.0, whereas the internal consistency was 0.91. The eight dimensions were safe practice environment (16 items), quality and quantity of staff (four items), salary and welfare (seven items), professional specialization and teamwork (seven items), work simplification (five items), informatics (five items), career development (nine items), and support and caring (12 items). The overall load for the indicators was 77.57%. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: The developed indicators may be used to evaluate the quality of nursing work environments. Furthermore, the indicators may be used in hospital surveys to establish baseline conditions and for outcome research that measures improvement in nursing work environments after 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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".