Factors influencing health-related quality of life among long-term care residents experiencing pain: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Pain is highly burdensome, affecting over 30% of long-term care (LTC) residents. Pain significantly reduces residents' health-related quality of life (HRQoL), limits their ability to perform activities of daily living (ADLs), restricts their social activities, and can lead to hopelessness, depression, and unnecessary healthcare costs. Although pain can generally be prevented or treated, eliminating pain may not always be possible, especially when residents have multiple chronic conditions. Therefore, improving the HRQoL of LTC residents with pain is a priority goal. Understanding factors influencing HRQoL of LTC residents with pain is imperative to designing and evaluating targeted interventions that complement pain management to improve residents' HRQoL. However, these factors are poorly understood, and we lack syntheses of available research on this topic. This systematic review protocol outlines the methods to identify, synthesize, and evaluate the available evidence on these factors. METHODS: This mixed methods review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We will systematically search Medline, EMBASE, PsycINFO, CINAHL, Scopus, Cochrane Database of Systematic Reviews and ProQuest Dissertation and Thesis Global from database inception. We will include primary studies and systematically conducted reviews without restrictions to language, publication date, and study design. We will also include gray literature (dissertation and reports) and search relevant reviews and reference lists of all included studies. Two reviewers will independently screen articles, conduct quality appraisal, and extract data. We will synthesize results thematically and conduct meta-analyses if statistical pooling is possible. Residents and family/friend caregivers will assist with interpreting the findings. DISCUSSION: This proposed systematic review will address an important knowledge gap related to the available evidence on factors influencing HRQoL of LTC residents with pain. Findings will be crucial for researchers, LTC administrators, and policy makers in uncovering research needs and in planning, developing, and evaluating strategies in addition to and complementary with pain management to help improve HRQoL among LTC residents with pain. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023405425.
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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 | 0.085 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.010 |
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".