Preparing future physicians for complexity: a post-graduate elective in HIV psychiatry
Bibliographic record
Abstract
BACKGROUND: Patients with complex care needs have multiple concurrent conditions (medical, psychiatric, social vulnerability or functional impairment), interfering with achieving desired health outcomes. Their care often requires coordination and integration of services across hospital and community settings. Physicians feel ill-equipped and unsupported to navigate uncertainty and ambiguity caused by multiple problems. A HIV Psychiatry resident elective was designed to support acquisition of integrated competencies to navigate uncertainty and disjointed systems of care - necessary for complex patient care. METHODS: Through qualitative thematic analysis of pre- and post-interviews with 12 participants - residents and clinic staff - from December 2019 to September 2022, we explored experiences of this elective. RESULTS: This educational experience helped trainees expand their understanding of what makes patients complex. Teachers and trainees emphasize the importance of an approach to "not knowing" and utilizing integrative competencies for navigating uncertainty. Through perspective exchange and collaboration, trainees showed evidence of adaptive expertise: the ability to improvise while drawing on past knowledge. CONCLUSIONS: Postgraduate training experiences should be designed to facilitate skills for caring for complex patients. These skills help residents fill in practice gaps, improvise when standardization fails, and develop adaptive expertise. Going forward, findings will be used to inform this ongoing elective.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".