Using nominal group technique to assess chronic pain, patients' perceived challenges and needs in a community health region
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to better understand the experiences of people suffering from chronic pain in order to plan client-centred educational interventions. METHODS: People in the community with chronic pain were invited via newspapers, newsletters and e-mail discussion lists to participate in a needs assessment process and to attend an educational session at a local community college. Using the nominal group technique, which is a qualitative method of data gathering, 53 participants reported their perceived challenges and needs in dealing with chronic pain. Participants were randomly assigned to one of 10 groups ranging from three to seven people. Responses were pooled to develop an overall list of their major concerns and needs. RESULTS: Issues were classified into six priority areas: medical and treatments, problems with daily living, emotional distress, social issues, sleep disturbances and financial issues. Participants indicated they had difficulty finding accessible, effective and acceptable care. Many participants perceived their family physician or other health-care providers were not adequately meeting their health-care needs. Specifically, sleep disorders; feeling of depression, irritability, worry and anxiety were perceived as medical and treatment areas requiring improvement. In addition, participants sought greater validation of their lived experience of chronic pain. CONCLUSION: Participants perceived that their needs were not being met adequately. There is a need for further study on physician-patient communication and its impact on patient health status and disability.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".