Initiatives Targeting Health Care Professionals
Bibliographic record
Abstract
OBJECTIVES: Pain is often undertreated in older adult populations due to factors, such as insufficient continuing education and health care resources. Initiatives to increase knowledge about pain assessment and management are crucial for the incorporation of research evidence into practice. Knowledge translation (KT) studies on pain management for older adults and relevant knowledge users have been conducted; however, the wide variety of KT program formats and outcomes underscores a need to evaluate and systematically report on the relevant literature. MATERIALS AND METHODS: Using a 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 KT programs targeted towards older adults, their informal caregivers, and health care professionals were examined. Initiatives focusing on health care professionals are the focus of this review. Initiatives focusing on older adults are reported in a companion article. RESULTS: From an initial 21,669 search results, 172 studies met our inclusion criteria. These studies varied widely in focus and delivery format but the majority were associated with significant risk of bias. In this report, we are focusing on 124 studies targeting health care professionals; 48 studies involving initiatives targeting older adults are reported in a companion article. Moreover, most programs were classified as knowledge mobilization studies without an implementation component. Across all studies, knowledge user satisfaction with the initiative and the suitability of the material presented were most commonly assessed. Patient outcomes, however, were underemphasized in the literature. CONCLUSION: Patient and clinical outcomes must be a focus of future research to fully conceptualize the success of KT programs for older adult individuals. Without implementation plans, disseminated knowledge does not tend to translate effectively into practice.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".