The content validation of the Self-Reported Misuse, Abuse and Diversion of Prescription Opioids (SR-MAD) instrument for use in patients with acute or chronic pain
Bibliographic record
Abstract
BACKGROUND: Establishing content validity is an essential component of instrument development. OBJECTIVE: To assess the content validity and patient interpretation of the Self-Reported Misuse, Abuse and Diversion of Prescription Opioids (SR-MAD) instrument. METHODS: A cross-sectional, qualitative study was conducted in patients with chronic or acute pain. Patients were recruited from three patient groups (opioid naïve, known opioid abusers, and chronic opioid non-abusers). After patients completed the SR-MAD, they participated in an in-person cognitive interview to assess the patient's understanding of the instrument. Descriptive statistics and content analysis were performed. RESULTS: Fifty-seven patients (Wave 1: 20; Wave 2: 37) were enrolled and completed the SR-MAD and cognitive interview. Mean age was 54.5 ± 13.7 years (range 25-84) with 12.5 years of living with pain. The most common chronic pain conditions were back pain (68%), neck pain (32%), and osteoarthritis (25%). Overall, most patients understood the meaning of each question and were able to describe each item using their own words. Many patients reported that some questions were not applicable to them but understood the meaning of the questions as well as the need to ask questions about misuse, abuse, and diversion of opioid medications. Minor revisions to the SR-MAD wording, response options, recall period, and the definition of "opioid", were recommended by the patients in both waves. LIMITATIONS: Given its qualitative design, this study has a small sample size. Additionally, quantitative validation of the SR-MAD is needed. CONCLUSION: The SR-MAD, developed based on expert consensus and revised with patient input, is a 15-item self-report instrument that can be used to identify and monitor prescription opioid abuse, misuse, and diversion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".