Clinical practitioners’ views on the management of patients with medically unexplained physical symptoms (MUPS): a qualitative study
Bibliographic record
Abstract
OBJECTIVES: By identifying strategies that practicing physicians use in managing patients with medically unexplained physical symptoms (MUPS), we present an interim practical management guide (IPMG) that clinical practitioners may find useful in their clinical practices and that may help guide future research. DESIGN: A qualitative research study based on interview data from practicing physicians with experience in dealing with MUPS and known to the physician members of the research team. A parallel exploration of patient experiences was carried out simultaneously and is reported elsewhere. SETTING: 2 urban centres in 2 different Canadian provinces in a healthcare system where family physicians provide the majority of primary care and self-referral to specialists rarely occurs. PARTICIPANTS: The physician members of the research team invited practicing family and specialty physicians to participate in the study. RESULTS: We characterise the care of patients with MUPS in terms of a 4-part framework: (1) the challenge of diagnosis; (2) the challenge of management/treatment; (3) the importance of communication and (4) the importance of the therapeutic relationship. CONCLUSIONS: On the basis of the details in the different parts of the framework, we propose an IPMG that practitioners may find useful to facilitate the clinical care of patients with MUPS. The guide can be readily implemented into the practice of any physician who cares for patients with MUPS.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".