Factor analysis validates the cluster structure of the dendrogram underlying the Multidimensional Affect and Pain Survey (MAPS) and challenges the a priori classification of the descriptors in the McGill Pain Questionnaire (MPQ)
Bibliographic record
Abstract
The purpose of this study was to validate the content and structure of the Multidimensional Affect and Pain Survey (MAPS) by means of factor analysis. The 101-MAPS is based on a dendrogram obtained by cluster analysis and contains 30 clusters subsumed within three superclusters. If the MAPS is a valid questionnaire for the quantification of emotion and pain in patients, then factor analysis of patients' intensity ratings should produce factors which correspond to the cluster structure of the dendrogram. To confirm the structure of the dendrogram and hence, MAPS, factor analysis was applied to the responses by 100 outpatients diagnosed with early stage cancer. Principal components analysis of responses to the MAPS yielded six factors. In accordance with the hypothesis, 13 of the 17 clusters within the MAPS somatosensory pain supercluster loaded on three sensory factors: factor 1, severe sensory pain; factor 3, moderate sensory pain; and factor 6, numb/cold. Five of the eight clusters within the emotional pain supercluster loaded on factor 2, negative emotions. Four of the five clusters in the well-being supercluster loaded on factor 4, good health. Factor 5, manageable illness was loaded on by clusters from the well-being supercluster and the somatosensory pain supercluster. The homogeneity of the six factors found demonstrate the validity of the MAPS and the cluster structure of the dendrogram. MAPS proved sensitive to sex differences; women endorsed the negative emotions factor more strongly than did men. The MAPS factors were much more homogeneous than those reported in the literature for the McGill Pain Questionnaire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".