Using the Theory of Planned Behaviour to predict nurses’ intention to integrate research evidence into clinical decision‐making
Bibliographic record
Abstract
AIMS: Using an extended theory of planned behaviour, this article is a report of a study to identify the factors that influence nurses' intention to integrate research evidence into their clinical decision-making. BACKGROUND: Health professionals are increasingly asked to adopt evidence-based practice. The integration of research evidence in nurses' clinical decision-making would have an important impact on the quality of care provided for patients. Despite evidence supporting this practice and the availability of high quality research in the field of nursing, the gap between research and practice is still present. DESIGN: A predictive correlational study. METHODS: A total of 336 nurses working in a university hospital participated in this research. Data were collected in February and March 2008 by means of a questionnaire based on an extension of the theory of planned behaviour. Descriptive statistics of the model variables, Pearson correlations between all the variables and multiple linear regression analysis were performed. RESULTS/FINDINGS: Nurses' intention to integrate research findings into clinical decision-making can be predicted by moral norm, normative beliefs, perceived behavioural control and past behaviour. The moral norm is the most important predictor. Overall, the final model explains 70% of the variance in nurses' intention. CONCLUSION: The present study supports the use of an extended psychosocial theory for identifying the determinants of nurses' intention to integrate research evidence into their clinical decision-making. Interventions that focus on increasing nurses' perceptions that using research is their responsibility for ensuring good patient care and providing a supportive environment could promote an evidence-based nursing practice.
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".