Pain Management Practices by Nurses: An Application of the Knowledge, Attitude and Practices (KAP) Model
Bibliographic record
Abstract
Pain is one of the most common reasons that drive people to go to hospitals. It has been found that several factors affect the practices of pain management. In this regard, this study aimed at investigating the underlying determinants in terms of pain management practices. Based on reviewing the previous studies and the suggestions of the KAP model, it was hypothesized that the main elements of the KAP model (attitudes and knowledge) significantly predict the variation in the practices of nurses regarding pain management. A questionnaire comprising the KAP model' s constructs, i.e. knowledge and attitude towards pain management, as well as pain management practices, was used to collect data from 266 registered nurses (n=266) who are deemed competent in the management of patients' pain in the Jordanian public hospitals. The two constructs, attitude and knowledge, which are the main determinants of the KAP model were found to independently predict nurses' practices of managing patients' pain. Knowledge of pain management was found to be the strongest predictor. Additionally, it was found that about 69% of the variance in pain management could be explained by the constructs of the KAP model. Therefore, it is recommended that the Jordanian hospitals and universities focus on nurses' knowledge and attitude towards pain management in order to enhance their practices in the field of pain management.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".