Patient-centered care and interprofessional collaboration in medical resident education: Where we stand and where we need to go
Bibliographic record
Abstract
Abstract Patient centered care (PCC) and interprofessional collaboration (IPC) remain important goals for all healthcare systems. While these tenets are a cornerstone of training for nursing and allied health professionals (AHPs), their role in internal medicine resident (IMR) training is unstructured and limited. We performed a narrative review to answer two questions, firstly ‘what is known about the attitudes and behaviors of internal medicine (IM) physicians and trainees with respect to PCC and IPC and how does this compare to AHPs?’ and secondly, ‘what evidence based interventions have been trialed to promote PCC and IPC in medical training?’ We searched databases including Cochrane, Medline, Embase, CINAHL and MedPortal. We reviewed 102 publications and found that medical residents tend to value PCC less than non-physician trainees. Hierarchical professional attitudes and a poor understanding of AHP roles are barriers to IPC, whereas diminished time for direct patient care, neglect of the patient’s context and social determinants of health, and lack of self-reflection are barriers to PCC. Published educational interventions for IMRs and AHPs have included classroom sessions, structured ward- and clinic-based interprofessional (IP) work, post-discharge care, home visits, and reflective practice. Interventions were evaluated using questionnaires/surveys, focus groups, tests, primary outcome assessments and ethnographic analysis. The most promising interventions are those that allow learners time for multidisciplinary observation, holistic patient assessments, engagement in care transitions and reflective practice. Based on the review findings we have made recommendations for integration of IPC and PCC training into IMR curricula. Future educational interventions should allow IMR observerships in a multidisciplinary team, introduce residents to the patient’s environment through home visits, incorporate patient/family perspectives in care, and include narrative reflections as part of professional development. Based on our findings and recommendations, these experiences can provide IMRs with much-needed exposure to collaborative, patient-centric care early in postgraduate training.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".