Educational Interventions to Improve Cancer Pain Control: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Inadequate pain control is a dismaying reality in cancer patients. OBJECTIVES: To review studies on cancer pain control interventions, and describe their findings with respect to participants' attitudes and knowledge, pain management, and pain levels. DATA SOURCES: Computer searches were made in MEDLINE from January 1962, in PsychLIT from January 1974, and in CINAHL from January 1982 to August 1999, using a search strategy based on a combination of key words. STUDY SELECTION: Computerized listings from these sources contained 383, 26, and 85 articles, respectively. After exclusion of duplicates, abstracts, editorials, letters, and irrelevant articles, we retained for review 33 articles, of which 25 (76%) were interventions targeting health professionals, and 8 (24%) interventions targeting patients and family caregivers. DATA EXTRACTION: Study reports were reviewed using the following structured framework: Intervention Setting, Study Methods, Process Assessment, and Pain Outcome Assessment (Attitudes and Knowledge, Pain Management, and Pain Relief/Quality of Life). DATA SYNTHESIS AND CONCLUSION: Educational interventions can successfully improve cancer pain knowledge and attitudes of health care professionals, but without having much impact on patients' pain levels. The most promising avenue for improving cancer pain control in ambulatory settings may be brief, nursing interventions targeting patients in combination with a daily pain diary. This review suggests that further progress may occur through incorporating a systematic and valid method of documenting daily fluctuation in pain levels, and ensuring that documented uncontrolled pain is followed rapidly by clinical reassessment and dose adjustment.
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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".