The effectiveness of Pictorial Representation of Illness and Self Measure (PRISM) for the assessment of the suffering and quality of interpersonal relationships of patients with chronic pain
Bibliographic record
Abstract
BACKGROUND: Pictorial Representation of Illness and Self Measure (PRISM) is a tool that can be used to visualize and evaluate the burden of suffering caused by an illness. The aim of this study was to identify which aspects of the burden of chronic pain patients are associated with Self/illness separation (SIS), an indicator of the magnitude of suffering. We also examined the effectiveness of PRISM for evaluating changes in the relationships between patients and their medical care and significant others due to our inpatient treatment. METHODS: Seventy-two patients with chronic pain who were outpatients or admitted to the Department of Psychosomatic Medicine completed PRISM, depression and anxiety scales, and three types of pain-related self-assessment questionnaires (Brief Pain Inventory, Short-form McGill Pain Questionnaire, and Pain Catastrophizing Scale). Outpatients were queried at the time of outpatient visits and inpatients at the time of admission. In addition to PRISM disks related to illness, we asked each patient to place disks related to things important to them and their medical care. Of the inpatients, 31 did PRISM at the time of discharge. Among the reported important factors, which significant other was placed at the time of admission and discharge was evaluated. The distances of self/medical care separation (SMcS) and self/significant others separation (SSoS) were measured. RESULTS: Of the 21 scales measured, 10 showed a significant correlation with SIS. Factor analysis of these 10 scales extracted three factors, Life interferences, Negative affects, and Pain intensity. The SMcS and SSoS distances were shorter at discharge than at admission. CONCLUSIONS: PRISM for patients with chronic pain is an integrated evaluation method that reflects three aspects of pain. By adding medical care and significant others to the usual method of placing only illness on the sheet it became possible to assess changes in the quality of interpersonal relationships.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".