How to Diagnose and Manage “Difficult” Patients - Development of a Workshop for Interprofessional Audience
Bibliographic record
Abstract
Introduction More than 15% of patients who present to a primary care clinic are considered “difficult” yet interprofessional members of primary care clinics receive little training on how to diagnose and manage these patients. Objectives Become familiar with successful method of workshop development on how to diagnose and manage “difficult” patients to interprofessional audience of six community health centers. Aims The aim of the workshop was to enhance primary care providers’ capacity to diagnose and manage “difficult” patients as well as serve as a pilot program for a larger conference on managing “difficult” patients. Methods A half-day workshop was designed to fill this perceived need of community health providers to learn how to diagnose and manage “difficult” patients. The workshop consisted of didactic presentation and case based small group learning. This workshop served as a pilot program for the development of larger conference for community providers on managing “difficult” patients. Results The workshop was evaluated by participants. 100% of respondents agreed that the workshop was relevant to their work and 87.5% of respondents reported that the workshop will alter their clinical practice. Conclusion The workshop has met participants’ perceived learning needs as well as served as a pilot program for a larger conference on managing difficult patients.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".