Patients' perspectives on methods of assessing pain
Bibliographic record
Abstract
Pain questionnaires often serve as an assessment tool for initial consultations in chronic pain clinics. The Pain Management Unit (PMU) is a tertiary care centre in Halifax, Nova Scotia. A number of clinicians in the PMU have noted that some patients express that questionnaires are time consuming to complete and believe they are not used in a manner that is helpful to their health care. The effectiveness of questionnaire-based pain evaluation is an area of active research. Text-heavy questionnaires have been criticized for their reliance on literacy and for the format’s inability to facilitate patient self-expression. Other methods of assessing pain have been suggested, including those that use pictograms, photographs and technology. This study was designed to gauge patients’ opinions on the current pain assessment method used in the PMU. In addition, it aimed to evaluate if incorporating art and technology appealed to current patients. The ultimate goal of this study was to evaluate if improvements could be made to patients’ pain assessment experience. Thirty patients were interviewed following their initial consultation appointments at the PMU. Interviews were transcribed verbatim and analyzed using NVivo Software to look for themes expressed by research participants. The study yielded a total of 20 different themes, such as repetition within the questionnaires, and the patient’s desire to incorporate different technologies such as an iPad or computer. Recommendations are proposed based on these themes to help guide the creation or modification of pain assessment tools.
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 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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".