Fibromyalgia and the Therapeutic Relationship: Where Uncertainty Meets Attitude
Bibliographic record
Abstract
BACKGROUND: Fibromyalgia remains underdiagnosed and suboptimally treated even though it affects an estimated 3.3% of Canadians. The present study examines knowledge and attitudinal challenges affecting optimal care. METHODS: A mixed-methods approach was employed. Discussion groups, semistructured interviews and a quantitative online survey (five-point scale) were conducted (June 2007 to January 2008). Participants included 189 general practitioners (GPs) and 139 specialists (anesthesiologists, neurologists, physiatrists, psychiatrists and rheumatologists) distributed across Canada. Participants included 18 patients to enrich the scope of the findings. RESULTS: GPs reported insufficient knowledge and skill in diagnosing fibromyalgia, with not all believing it to be a diagnosable condition (mean 3.74/5). Twenty-three per cent of GPs and 12% of specialists characterized fibromyalgia patients as malingerers. They further reported a lack of knowledge and skill in treating fibromyalgia (mean 2.73/5), including the pain, sleep disorders and mood disorders related to the condition (mean 3.32/5). Specialists shared these challenges, although to a lesser degree - "We are not trained to treat distress and suffering" (Specialist). Attitudinal issues centred around frustration (mean 3.91⁄5) and negative profiling of fibromyalgia patients (mean 3.06/5 and 1.99/5). CONCLUSIONS: Findings revealed the presence of GP attitudinal and confidence challenges in caring for fibromyalgia patients. As care of fibromyalgia patients moves to general practices, these fundamental competencies must be addressed to assure that all patients receive the quality of care necessary to manage their disease and to empower physicians to be more professionally effective. As stated by one patient, "Why are we being penalized for having this disability?"
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".