Bibliographic record
Abstract
Purpose: This study aimed at the effectiveness to investigate the performance of evidence-based pain assessment and management guidelines. Methods: Participants were 140 nurses at the med-surgical units. Data were collected in early July, 2014 using Registered Nurses Association of Ontario (RNAO) guideline (2007) revised and validated by Hong and Lee (2012) and analyzed by descriptive statistics, t-test, ANOVA using SPSS/WIN18.0. Results: The score of performance of pain assessment guideline was higher than the score of pain management. Categories with high score were pain screening, parameter of pain assessment, documentation, assessment of opioids side-effects, and record of pain caused intervention. Categories with low score were comprehensive pain assessment, multidisciplinary communication, establishing a plan for pain management, consultation and education for patients and their families, and education for nurse. Non-pharmacological management was the lowest one. Conclusion: Assessing and managing pain is a complex phenomenon. It might be useful if institutions host training programs to ensure that nurse are better able to understand and implement pain assessment and management. Since non-pharmacological management is less likely to be used by nurses it may be helpful to include these methods in a training program.
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 | 0.032 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".