The Multimodal Assessment Model of Pain
Bibliographic record
Abstract
OBJECTIVES: Pain assessment is enigmatic. Although clinicians and researchers must rely upon observations to evaluate pain, the personal experience of pain is fundamentally unobservable. This raises the question of how the inherent subjectivity of pain can and should be integrated within assessment. Current models fail to tackle key facets of this problem, such as what essential aspects of pain are overlooked when we only rely on numeric forms of assessment, and what types of assessment need to be prioritized to ensure alignment with our conceptualization of pain as a subjective experience. We present the multimodal assessment model of pain (MAP) as offering practical frameworks for navigating these challenges. METHODS: This is a narrative review. RESULTS: MAP delineates qualitative (words, behaviors) and quantitative (self-reported measures, non-self-reported measures) assessment and regards the qualitative pain narrative as the best available root proxy for inferring pain in others. MAP offers frameworks to better address pain subjectivity by: (1) delineating separate criteria for identifying versus assessing pain. Pain is identified through narrative reports, while comprehensive assessment is used to infer why pain is reported; (2) integrating compassion-based and mechanism-based management by both validating pain reports and assessing underlying processes; (3) conceptualizing comprehensive pain assessment as both multidimensional and multimodal (listening/observing and measuring); and (4) describing how qualitative data help validate and contextualize quantitative pain measures. DISCUSSION: MAP is expected to help clinicians validate pain reports as important and legitimate, regardless of other findings, and help our field develop more comprehensive, valid, and compassionate approaches to assessing pain.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".