Understanding Work Contextual Factors: A Short‐Cut to Evidence‐Based Practice?
Bibliographic record
Abstract
BACKGROUND: It has become increasingly clear that workplace contextual factors make an important contribution to provider and patient outcomes. The potential for health care professionals of using research in practice is also linked to such factors, although the exact factors or mechanisms for enhancing this potential are not understood. From a perspective of implementing evidence-based nursing practice, the authors of this article report on a study examining contextual factors. AIM: The objective of this study was to identify predictors of organizational improvement by measuring staff perceptions of work contextual factors. METHOD: The Quality Work Competence questionnaire was used in a repeated measurement survey with a 1-year break between the two periods of data collection. The sample consisted of 134 employees from four neonatal units in Sweden. FINDINGS: Over the study period significant changes occurred among staff perceptions, both within and between units, on various factors. Changes in staff perceptions on skills development and participatory management were the major predictors of enhanced potential of overall organizational improvement. Perceived improvement in skills development and performance feedback predicted improvement in leadership. Change in commitment was predicted by perceived decreases in work tempo and work-related exhaustion. CONCLUSIONS AND IMPLICATIONS: These findings indicate the potential for organizational improvement by developing a learning and supportive professional environment as well as by involving staff in decision-making at the unit level. Such initiatives are also likely to be of importance for enhanced use of research in practice and evidence-based nursing. On the other hand, high levels of work tempo and burnout appear to have negative consequences on staff commitment for improving care and the work environment. A better understanding of workplace contextual factors is necessary for improving the organizational potential of getting research into practice and should be considered in future implementation projects.
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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 | 0.075 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".