Determinants of nurses' intention to administer opioids for pain relief
Bibliographic record
Abstract
A statewide cross-sectional survey was conducted in Australia to identify the determinants of registered nurses' intention to administer opioids to patients with pain. Attitudes, subjective norms and perceived control, the key determinants of the Theory of Planned Behavior, were found to independently predict nurses' intention to administer opioids to these patients. Perceived control was the strongest predictor. Nurses reported positive overall attitudes towards opioids and their use in pain management. However, many negative attitudes were identified; for example, administering the least amount of opioid and encouraging patients to have non-opioids rather than opioids for pain relief. The findings related to specific attitudes and normative pressures provide insight into registered nurses' management of pain for hospitalized patients and the direction for educational interventions to improve registered nurses' administration of opioids for pain management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".