A brief analysis of Clinical Best Practice Guidelines: Assessment and Management of Pain (Third Edition) by Registered Nurses' Association of Ontario in 2013
Bibliographic record
Abstract
Mismanagement of pain is a burden on individuals, the health care system and the society. Pain is related to a person's life, which seriously affects the patient's physical health and quality of life. This article interprets the main content of Clinical Best Practice Guidelines: Assessment and Management of Pain (Third Edition) published by Registered Nurses' Association of Ontario (RNAO) in 2013, to help clinical nurses get the latest literature of pain assessment and management, so as to improve the level of care and reduce pain in patients. The updated guidelines emphasize that anyone's pain should be recognized and respected, and that individualized pain assessment and management should include relevant biopsycho-social components. In addition, this guideline advocates continuous learning by health care professionals to update knowledge and information on pain assessment and management, and encourages communication among professional teams in order to optimize pain assessment and management. Key words: Practice guideline; Assessment and management of pain; Interpretation
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 | 0.028 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".