Pain Assessment for Nursing Home Residents
Bibliographic record
Abstract
BACKGROUND: The burden of pain in nursing homes is substantial; however, pain assessment for both acute and chronic conditions remains inadequate, resulting in inappropriate or inadequate treatment. Complexities in assessing resident pain have been attributed to factors (barriers and facilitators) arising at the resident, healthcare provider, and healthcare system levels. OBJECTIVES: In this systematic review protocol, we identify our research approach that will be used to critically appraise and synthesize data in order to assess barriers and facilitators to pain assessment in nursing home residents aged ≥65 years. METHODS: This is a Cochrane style systematic review protocol adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols reporting standards. This review will include primary (original) qualitative literature concerning either barriers or facilitators to pain assessment in older adult nursing home residents. A thematic analysis approach will be employed in collating and summarizing included data and will be categorized into resident, healthcare provider, and system-level factors. Database searches will include Abstracts in Social Gerontology, CINAHL, Cochrane Central Register of Controlled Trials, Embase, MEDLINE, and Web of Science. DISCUSSION: The identification of barriers and facilitators to pain assessment in older adult nursing home residents may assist healthcare providers across all platforms and levels of education to improve pain assessment among nursing home residents. Improving the assessment of pain has the potential to improve quality of care and ultimately quality of life for older adult nursing home residents.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".