Patients’ experiences of chronic non-malignant musculoskeletal pain: a qualitative systematic review
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal (MSK) pain is one of the most predominant types of pain and accounts for a large portion of the primary care workload. AIM: To systematically review and integrate the findings of qualitative research to increase understanding of patients' experiences of chronic non-malignant MSK pain. DESIGN AND SETTING: Synthesis of qualitative research using meta-ethnography using six electronic databases up until February 2012 (Medline, Embase, Cinahl, Psychinfo, Amed and HMIC). METHOD: Databases were searched from their inception until February 2012, supplemented by hand-searching contents lists of specific journals for 2001-2011 and citation tracking. Full published reports of qualitative studies exploring adults' own experience of chronic non-malignant MSK pain were eligible for inclusion. RESULTS: Out of 24 992 titles, 676 abstracts, and 321 full texts were screened, 77 papers reporting 60 individual studies were included. A new concept of pain as an adversarial struggle emerged. This adversarial struggle was to: 1) affirm self; 2) reconstruct self in time; 3) construct an explanation for suffering; 4) negotiate the healthcare system; and 5) prove legitimacy. However, despite this struggle there is also a sense for some patients of 6) moving forward alongside pain. CONCLUSIONS: This review provides a theoretical underpinning for improving patient experience and facilitating a therapeutic collaborative partnership. A conceptual model is presented, which offers opportunities for improvement by involving patients, showing them their pain is understood, and forming the basis to help patients move forward alongside their pain.
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 | 0.054 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".