An Integrative Review of the Literature on Pain Management Barriers: Implications for the Canadian Clinical Context
Bibliographic record
Abstract
Despite decades of pain research, substandard pain management continues to be distressingly prevalent across health-care settings. This integrative literature review analyzes and synthesizes barriers to effective pain management and identifies areas for future investigation in a Canadian context. Three sets of key barriers were identified through thematic analysis of 24 original research studies published in the period 2003-13: patient, professional, and organizational. These barriers rarely occurred in isolation, with many studies reporting examples in all three categories. This suggests that interventions need to reflect the multifactorial nature of pain management. Reframing pain education as a public health initiative could lead to sustainable improvement, as could the strengthening of partnerships between patients and health-care providers. There are tremendous opportunities for the advanced practice nurse to take a lead in pain management. The delivery of high-quality care that encompasses effective pain management strategies must be a priority for nursing. Research approaches, such as pragmatic mixed methods, that offer contextual understanding of how pain is managed are suggested.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.044 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".