Initiatives Targeting Patients
Bibliographic record
Abstract
OBJECTIVES: Older adults frequently experience persistent pain but are often unaware of self-management and other strategies that can help improve their condition. In a related article, we reported on pain assessment and management knowledge translation initiatives related to older adults that were targeting health professionals. In this paper, we report on initiatives targeting older adults. MATERIALS AND METHODS: Using systematic review methodology, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE, PsycInfo, and Web of Science databases were searched from inception to June 2023. Pain-related knowledge translation programs targeted towards older adults, their informal caregivers, and health care professionals were examined. RESULTS: From an initial 21,669 search results, 172 studies met our inclusion criteria. Of these studies, 49 targeted older adults. These studies varied widely in focus and delivery format, but the majority were associated with a significant risk of bias. Older adults with musculoskeletal pain were the primary recipients of education. Most programs were classified as knowledge mobilization initiatives. The remainder were considered self-management programs. Knowledge users were satisfied with the suitability of the information presented, and patient outcomes were a primary concern across all studies. Behavioral changes and pain management outcomes, however, were underemphasized in the literature. CONCLUSION: Knowledge acquisition outcomes are overrepresented in the current literature compared with behavioral outcomes changes. Nonetheless, older adults report benefitting from the guidance provided in self-management programs when applying knowledge to practice. Future research is needed to better understand the facilitators and barriers to pain management changes in this population.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".