Applying the Theory of Planned Behaviour to understand nurse intention to follow recommendations related to a preventive clinical practice
Bibliographic record
Abstract
The use of filter needles reduces the number of particles found in parenteral solutions after the opening of glass ampoules and has been recommended by many authors. Even so, nurses do not use filter needles unilaterally in their practice. In order to understand the psychosocial determinants of nurse intention to follow recommendations related to the use of filter needles in the preparation of parenteral medication, we conducted a cross-sectional study in a large university medical centre in the province of Quebec (Canada). We developed a questionnaire based on Ajzen’s Theory of Planned Behaviour and distributed it to all nurses ( n = 364) from eight care units. A total of 242 questionnaires were completed and returned (response rate of 66.5%). Attitude towards the behaviour and perceived behavioural control predicted nurse intention to use filter needles according to recommendations. Three specific beliefs related to these variables – ease of use, enjoyment and reason – explained 50.3% of the variance in nurse intention to use filter needles. The results of this study support the use of the Theory of Planned Behaviour as a theoretical basis that can help identify important avenues to inform behaviour change strategies regarding healthcare professional adoption of guidelines to improve patient safety.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".