Transforming Long-Term Care Pain Management in North America: The Policy–Clinical Interface: Table 1
Bibliographic record
Abstract
BACKGROUND: The undertreatment of pain in older adults who reside in long-term care (LTC) facilities has been well documented, leading to clinical guideline development and professional educational programs designed to foster better pain assessment and management in this population. Despite these efforts, little improvement has occurred, and we postulate that focused attention to public policy and cost implications of systemic change is required to create positive pain-related outcomes. OBJECTIVE: Our goal was to outline feasible and cost-effective clinical and public policy recommendations designed to address the undermanagement of pain in LTC facilities. METHODS: We arranged a 2-day consensus meeting of prominent United States and Canadian pain and public policy experts. An initial document describing the problem of pain undermanagement in LTC was developed and circulated prior to the meeting. Participants were also asked to respond to a list of relevant questions before arriving. Following formal presentations of a variety of proposals and extensive discussion among clinicians and policy experts, a set of recommendations was developed. RESULTS AND CONCLUSIONS: We outline key elements of a transformational model of pain management in LTC for the United States and Canada. Consistent with previously formulated clinical guidelines but with attention to readily implementable public policy change in both countries, this transformational model of LTC has important implications for LTC managers and policy makers as well as major quality of life implications for LTC 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".