Patient involvement in assessment of postgraduate medical learners: A scoping review
Bibliographic record
Abstract
CONTEXT: Competency-based assessment of learners may benefit from a more holistic, inclusive, approach for determining readiness for unsupervised practice. However, despite movements towards greater patient partnership in health care generally, inclusion of patients in postgraduate medical learners' assessment is largely absent. METHODS: We conducted a scoping review to map the nature, extent and range of literature examining the inclusion (or exclusion) of patients within the assessment of postgraduate medical learners. Guided by Arskey and O'Malley's framework and informed by Levac et al. and Thomas et al., we searched two databases (MEDLINE® and Embase®) from inception until February 2021 using subheadings related to assessment, patients and postgraduate learners. Data analysis examined characteristics regarding the nature and factor influencing patient involvement in assessment. RESULTS: We identified 41 papers spanning four decades. Some literature suggests patients are willing to be engaged in assessment, however choose not to engage when, for example, language barriers may exist. When stratified by specialty or clinical setting, the influence of factors such as gender, race, ethnicity or medical condition seems to remain consistent. Patients may participate in assessment as a stand-alone group or part of a multi-source feedback process. Patients generally provided high ratings but commented on the observed professional behaviours and communication skills in comparison with physicians who focused on medical expertise. CONCLUSION: Factors that influence patient involvement in assessment are multifactorial including patients' willingness themselves, language and reading-comprehension challenges and available resources for training programmes to facilitate the integration of patient assessments. These barriers however are not insurmountable. While understudied, research examining patient involvement in assessment is increasing; however, our review suggests that the extent which the unique insights will be taken up in postgraduate medical education may be dependent on assessment systems readiness and, in particular, physician readiness to partner with patients in this way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".